<\/p>\n
Values determined by this site’s code when the information was collected. Dates are JST.<\/em><\/p>\n<\/p>\n<\/p>\nWhat we checked ourselves<\/strong><\/p>\n\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/li>\n<\/ul>\nDetails and conditions are in \u201cOur Own Measurements\u201d below.<\/p>\n<\/p>\nOverview<\/h2>\nAn implementation and model for the MLX framework, harshatheg\/Qwen-2.5-1B-RLCD<\/code>, has been released to achieve high-efficiency inference in Apple Silicon environments. This repository adopts Qwen\/Qwen2.5-1.5B-Instruct<\/code> as its base model and provides an inference engine incorporating parallel constrained decoding.<\/p>\nTraditional autoregressive decoding generates tokens one by one in sequence, which causes latency to increase as schemas grow larger. By evaluating multi-field JSON schemas and choices (enum, boolean) in parallel, this method achieves a 5.6x to 7.0x latency reduction and 100% schema validity on an Apple Silicon M4 Max.<\/p>\nSpecifications<\/h2>\n\nBase Model: Qwen\/Qwen2.5-1.5B-Instruct<\/li>\nParameter Count: 1.54B (Non-embedding parameters: 1.31B)<\/li>\nContext Length: Full 32,768 tokens (up to 8,192 generation tokens)<\/li>\nLicense: apache-2.0<\/li>\n<\/ul>\nPerformance<\/h2>\nAccording to measurements published by the author using mlx-community\/Qwen2.5-1.5B-Instruct-4bit<\/code> on an Apple Silicon M4 Max (macOS Sequoia) environment, the comparison between the standard autoregressive baseline and parallel constrained decoding is as follows:<\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nScenario<\/th>\nFields<\/th>\nAutoregressive Baseline<\/th>\nParallel Constrained<\/th>\nLatency Speedup<\/th>\nSyntax Validity<\/th>\n<\/tr>\n<\/thead>\n\n\nFintech Fraud Routing<\/td>\n4 fields<\/td>\n420 ms (120 tok\/s)<\/td>\n75 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nCode Security Audit<\/td>\n4 fields<\/td>\n380 ms (125 tok\/s)<\/td>\n68 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nHigh-Cardinality Tariff<\/td>\n1 field (255 choices)<\/td>\n500 ms (118 tok\/s)<\/td>\n89 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nEnterprise Support Triage<\/td>\n28 fields<\/td>\n1,900 ms (130 tok\/s)<\/td>\n270 ms<\/td>\n7.0x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe author’s measurements show that the effect of parallel processing becomes more pronounced in scenarios with an increasing number of target fields. For “Enterprise Support Triage,” which extracts 28 fields, conventional autoregressive generation required 1,900 ms, whereas this method achieved a significant speedup of 7.0x at 270 ms.<\/p>\nAdditionally, consistent speedups of 5.6x (75 ms to 89 ms) were recorded for the high-cardinality “High-Cardinality Tariff” with 255 choices, as well as the 4-field “Fintech Fraud Routing” and “Code Security Audit.” In all test cases, syntax validity is 100% guaranteed, and the strength lies in executing high-speed processing while completely preventing grammar errors and missing fields during structured data output.<\/p>\nStrengths and Use Cases<\/h2>\nBuilding upon the high instruction-following and structured data comprehension capabilities of the base model Qwen\/Qwen2.5-1.5B-Instruct<\/code>, this combines parallel constrained decoding technology to exhibit specialized strengths in information extraction and classification tasks.<\/p>\nSpecifically, input context and schema definition text are preloaded (prefilled) into the MLX KV Cache (Key-Value Cache) just once, and that state is broadcast in parallel to the evaluation of each field. By applying mask extraction (logit slicing) only to token IDs permitted for each field to directly calculate probabilities from predefined choices (booleans or up to 255 enums), unnecessary token generation loops are avoided.<\/p>\nThis makes it highly effective in business scenarios such as:\n– Fintech fraud routing (identifying risk levels and determining the necessity of confirmation)\n– Security audits (classifying vulnerable code and assigning remediation priorities)\n– Automated triage of large-scale customer support tickets (department routing and urgency setting)\n– Identifying product codes or tariff classifications (such as HS codes) with numerous classification candidates<\/p>\nSince JSON syntax breaks or unnecessary key outputs (hallucinations) do not occur by design, it is extremely well-suited for integration with backend systems and the construction of automated pipelines where accuracy is required.<\/p>\n<\/p>\nHardware Requirements<\/h2>\nEstimated requirements (calculated by Local Model Watch)<\/strong> \u2014 1.5B parameters (taken from the base model Qwen\/Qwen2.5-1.5B-Instruct)<\/p>\n\n\n\n\nYour VRAM<\/th>\nQuantization<\/th>\nFile size<\/th>\nEst. memory needed<\/th>\n<\/tr>\n<\/thead>\n\n\n4GB (laptop iGPU \/ phone class)<\/td>\nBF16<\/td>\n2.9GB<\/td>\n3.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nMemory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
What we checked ourselves<\/strong><\/p>\n\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/li>\n<\/ul>\nDetails and conditions are in \u201cOur Own Measurements\u201d below.<\/p>\n<\/p>\nOverview<\/h2>\nAn implementation and model for the MLX framework, harshatheg\/Qwen-2.5-1B-RLCD<\/code>, has been released to achieve high-efficiency inference in Apple Silicon environments. This repository adopts Qwen\/Qwen2.5-1.5B-Instruct<\/code> as its base model and provides an inference engine incorporating parallel constrained decoding.<\/p>\nTraditional autoregressive decoding generates tokens one by one in sequence, which causes latency to increase as schemas grow larger. By evaluating multi-field JSON schemas and choices (enum, boolean) in parallel, this method achieves a 5.6x to 7.0x latency reduction and 100% schema validity on an Apple Silicon M4 Max.<\/p>\nSpecifications<\/h2>\n\nBase Model: Qwen\/Qwen2.5-1.5B-Instruct<\/li>\nParameter Count: 1.54B (Non-embedding parameters: 1.31B)<\/li>\nContext Length: Full 32,768 tokens (up to 8,192 generation tokens)<\/li>\nLicense: apache-2.0<\/li>\n<\/ul>\nPerformance<\/h2>\nAccording to measurements published by the author using mlx-community\/Qwen2.5-1.5B-Instruct-4bit<\/code> on an Apple Silicon M4 Max (macOS Sequoia) environment, the comparison between the standard autoregressive baseline and parallel constrained decoding is as follows:<\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nScenario<\/th>\nFields<\/th>\nAutoregressive Baseline<\/th>\nParallel Constrained<\/th>\nLatency Speedup<\/th>\nSyntax Validity<\/th>\n<\/tr>\n<\/thead>\n\n\nFintech Fraud Routing<\/td>\n4 fields<\/td>\n420 ms (120 tok\/s)<\/td>\n75 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nCode Security Audit<\/td>\n4 fields<\/td>\n380 ms (125 tok\/s)<\/td>\n68 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nHigh-Cardinality Tariff<\/td>\n1 field (255 choices)<\/td>\n500 ms (118 tok\/s)<\/td>\n89 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nEnterprise Support Triage<\/td>\n28 fields<\/td>\n1,900 ms (130 tok\/s)<\/td>\n270 ms<\/td>\n7.0x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe author’s measurements show that the effect of parallel processing becomes more pronounced in scenarios with an increasing number of target fields. For “Enterprise Support Triage,” which extracts 28 fields, conventional autoregressive generation required 1,900 ms, whereas this method achieved a significant speedup of 7.0x at 270 ms.<\/p>\nAdditionally, consistent speedups of 5.6x (75 ms to 89 ms) were recorded for the high-cardinality “High-Cardinality Tariff” with 255 choices, as well as the 4-field “Fintech Fraud Routing” and “Code Security Audit.” In all test cases, syntax validity is 100% guaranteed, and the strength lies in executing high-speed processing while completely preventing grammar errors and missing fields during structured data output.<\/p>\nStrengths and Use Cases<\/h2>\nBuilding upon the high instruction-following and structured data comprehension capabilities of the base model Qwen\/Qwen2.5-1.5B-Instruct<\/code>, this combines parallel constrained decoding technology to exhibit specialized strengths in information extraction and classification tasks.<\/p>\nSpecifically, input context and schema definition text are preloaded (prefilled) into the MLX KV Cache (Key-Value Cache) just once, and that state is broadcast in parallel to the evaluation of each field. By applying mask extraction (logit slicing) only to token IDs permitted for each field to directly calculate probabilities from predefined choices (booleans or up to 255 enums), unnecessary token generation loops are avoided.<\/p>\nThis makes it highly effective in business scenarios such as:\n– Fintech fraud routing (identifying risk levels and determining the necessity of confirmation)\n– Security audits (classifying vulnerable code and assigning remediation priorities)\n– Automated triage of large-scale customer support tickets (department routing and urgency setting)\n– Identifying product codes or tariff classifications (such as HS codes) with numerous classification candidates<\/p>\nSince JSON syntax breaks or unnecessary key outputs (hallucinations) do not occur by design, it is extremely well-suited for integration with backend systems and the construction of automated pipelines where accuracy is required.<\/p>\n<\/p>\nHardware Requirements<\/h2>\nEstimated requirements (calculated by Local Model Watch)<\/strong> \u2014 1.5B parameters (taken from the base model Qwen\/Qwen2.5-1.5B-Instruct)<\/p>\n\n\n\n\nYour VRAM<\/th>\nQuantization<\/th>\nFile size<\/th>\nEst. memory needed<\/th>\n<\/tr>\n<\/thead>\n\n\n4GB (laptop iGPU \/ phone class)<\/td>\nBF16<\/td>\n2.9GB<\/td>\n3.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nMemory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Details and conditions are in \u201cOur Own Measurements\u201d below.<\/p>\n
An implementation and model for the MLX framework, harshatheg\/Qwen-2.5-1B-RLCD<\/code>, has been released to achieve high-efficiency inference in Apple Silicon environments. This repository adopts Qwen\/Qwen2.5-1.5B-Instruct<\/code> as its base model and provides an inference engine incorporating parallel constrained decoding.<\/p>\nTraditional autoregressive decoding generates tokens one by one in sequence, which causes latency to increase as schemas grow larger. By evaluating multi-field JSON schemas and choices (enum, boolean) in parallel, this method achieves a 5.6x to 7.0x latency reduction and 100% schema validity on an Apple Silicon M4 Max.<\/p>\nSpecifications<\/h2>\n\nBase Model: Qwen\/Qwen2.5-1.5B-Instruct<\/li>\nParameter Count: 1.54B (Non-embedding parameters: 1.31B)<\/li>\nContext Length: Full 32,768 tokens (up to 8,192 generation tokens)<\/li>\nLicense: apache-2.0<\/li>\n<\/ul>\nPerformance<\/h2>\nAccording to measurements published by the author using mlx-community\/Qwen2.5-1.5B-Instruct-4bit<\/code> on an Apple Silicon M4 Max (macOS Sequoia) environment, the comparison between the standard autoregressive baseline and parallel constrained decoding is as follows:<\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nScenario<\/th>\nFields<\/th>\nAutoregressive Baseline<\/th>\nParallel Constrained<\/th>\nLatency Speedup<\/th>\nSyntax Validity<\/th>\n<\/tr>\n<\/thead>\n\n\nFintech Fraud Routing<\/td>\n4 fields<\/td>\n420 ms (120 tok\/s)<\/td>\n75 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nCode Security Audit<\/td>\n4 fields<\/td>\n380 ms (125 tok\/s)<\/td>\n68 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nHigh-Cardinality Tariff<\/td>\n1 field (255 choices)<\/td>\n500 ms (118 tok\/s)<\/td>\n89 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nEnterprise Support Triage<\/td>\n28 fields<\/td>\n1,900 ms (130 tok\/s)<\/td>\n270 ms<\/td>\n7.0x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe author’s measurements show that the effect of parallel processing becomes more pronounced in scenarios with an increasing number of target fields. For “Enterprise Support Triage,” which extracts 28 fields, conventional autoregressive generation required 1,900 ms, whereas this method achieved a significant speedup of 7.0x at 270 ms.<\/p>\nAdditionally, consistent speedups of 5.6x (75 ms to 89 ms) were recorded for the high-cardinality “High-Cardinality Tariff” with 255 choices, as well as the 4-field “Fintech Fraud Routing” and “Code Security Audit.” In all test cases, syntax validity is 100% guaranteed, and the strength lies in executing high-speed processing while completely preventing grammar errors and missing fields during structured data output.<\/p>\nStrengths and Use Cases<\/h2>\nBuilding upon the high instruction-following and structured data comprehension capabilities of the base model Qwen\/Qwen2.5-1.5B-Instruct<\/code>, this combines parallel constrained decoding technology to exhibit specialized strengths in information extraction and classification tasks.<\/p>\nSpecifically, input context and schema definition text are preloaded (prefilled) into the MLX KV Cache (Key-Value Cache) just once, and that state is broadcast in parallel to the evaluation of each field. By applying mask extraction (logit slicing) only to token IDs permitted for each field to directly calculate probabilities from predefined choices (booleans or up to 255 enums), unnecessary token generation loops are avoided.<\/p>\nThis makes it highly effective in business scenarios such as:\n– Fintech fraud routing (identifying risk levels and determining the necessity of confirmation)\n– Security audits (classifying vulnerable code and assigning remediation priorities)\n– Automated triage of large-scale customer support tickets (department routing and urgency setting)\n– Identifying product codes or tariff classifications (such as HS codes) with numerous classification candidates<\/p>\nSince JSON syntax breaks or unnecessary key outputs (hallucinations) do not occur by design, it is extremely well-suited for integration with backend systems and the construction of automated pipelines where accuracy is required.<\/p>\n<\/p>\nHardware Requirements<\/h2>\nEstimated requirements (calculated by Local Model Watch)<\/strong> \u2014 1.5B parameters (taken from the base model Qwen\/Qwen2.5-1.5B-Instruct)<\/p>\n\n\n\n\nYour VRAM<\/th>\nQuantization<\/th>\nFile size<\/th>\nEst. memory needed<\/th>\n<\/tr>\n<\/thead>\n\n\n4GB (laptop iGPU \/ phone class)<\/td>\nBF16<\/td>\n2.9GB<\/td>\n3.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nMemory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
harshatheg\/Qwen-2.5-1B-RLCD<\/code>, has been released to achieve high-efficiency inference in Apple Silicon environments. This repository adopts Qwen\/Qwen2.5-1.5B-Instruct<\/code> as its base model and provides an inference engine incorporating parallel constrained decoding.<\/p>\nTraditional autoregressive decoding generates tokens one by one in sequence, which causes latency to increase as schemas grow larger. By evaluating multi-field JSON schemas and choices (enum, boolean) in parallel, this method achieves a 5.6x to 7.0x latency reduction and 100% schema validity on an Apple Silicon M4 Max.<\/p>\nSpecifications<\/h2>\n\nBase Model: Qwen\/Qwen2.5-1.5B-Instruct<\/li>\nParameter Count: 1.54B (Non-embedding parameters: 1.31B)<\/li>\nContext Length: Full 32,768 tokens (up to 8,192 generation tokens)<\/li>\nLicense: apache-2.0<\/li>\n<\/ul>\nPerformance<\/h2>\nAccording to measurements published by the author using mlx-community\/Qwen2.5-1.5B-Instruct-4bit<\/code> on an Apple Silicon M4 Max (macOS Sequoia) environment, the comparison between the standard autoregressive baseline and parallel constrained decoding is as follows:<\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nScenario<\/th>\nFields<\/th>\nAutoregressive Baseline<\/th>\nParallel Constrained<\/th>\nLatency Speedup<\/th>\nSyntax Validity<\/th>\n<\/tr>\n<\/thead>\n\n\nFintech Fraud Routing<\/td>\n4 fields<\/td>\n420 ms (120 tok\/s)<\/td>\n75 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nCode Security Audit<\/td>\n4 fields<\/td>\n380 ms (125 tok\/s)<\/td>\n68 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nHigh-Cardinality Tariff<\/td>\n1 field (255 choices)<\/td>\n500 ms (118 tok\/s)<\/td>\n89 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nEnterprise Support Triage<\/td>\n28 fields<\/td>\n1,900 ms (130 tok\/s)<\/td>\n270 ms<\/td>\n7.0x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe author’s measurements show that the effect of parallel processing becomes more pronounced in scenarios with an increasing number of target fields. For “Enterprise Support Triage,” which extracts 28 fields, conventional autoregressive generation required 1,900 ms, whereas this method achieved a significant speedup of 7.0x at 270 ms.<\/p>\nAdditionally, consistent speedups of 5.6x (75 ms to 89 ms) were recorded for the high-cardinality “High-Cardinality Tariff” with 255 choices, as well as the 4-field “Fintech Fraud Routing” and “Code Security Audit.” In all test cases, syntax validity is 100% guaranteed, and the strength lies in executing high-speed processing while completely preventing grammar errors and missing fields during structured data output.<\/p>\nStrengths and Use Cases<\/h2>\nBuilding upon the high instruction-following and structured data comprehension capabilities of the base model Qwen\/Qwen2.5-1.5B-Instruct<\/code>, this combines parallel constrained decoding technology to exhibit specialized strengths in information extraction and classification tasks.<\/p>\nSpecifically, input context and schema definition text are preloaded (prefilled) into the MLX KV Cache (Key-Value Cache) just once, and that state is broadcast in parallel to the evaluation of each field. By applying mask extraction (logit slicing) only to token IDs permitted for each field to directly calculate probabilities from predefined choices (booleans or up to 255 enums), unnecessary token generation loops are avoided.<\/p>\nThis makes it highly effective in business scenarios such as:\n– Fintech fraud routing (identifying risk levels and determining the necessity of confirmation)\n– Security audits (classifying vulnerable code and assigning remediation priorities)\n– Automated triage of large-scale customer support tickets (department routing and urgency setting)\n– Identifying product codes or tariff classifications (such as HS codes) with numerous classification candidates<\/p>\nSince JSON syntax breaks or unnecessary key outputs (hallucinations) do not occur by design, it is extremely well-suited for integration with backend systems and the construction of automated pipelines where accuracy is required.<\/p>\n<\/p>\nHardware Requirements<\/h2>\nEstimated requirements (calculated by Local Model Watch)<\/strong> \u2014 1.5B parameters (taken from the base model Qwen\/Qwen2.5-1.5B-Instruct)<\/p>\n\n\n\n\nYour VRAM<\/th>\nQuantization<\/th>\nFile size<\/th>\nEst. memory needed<\/th>\n<\/tr>\n<\/thead>\n\n\n4GB (laptop iGPU \/ phone class)<\/td>\nBF16<\/td>\n2.9GB<\/td>\n3.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nMemory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Qwen\/Qwen2.5-1.5B-Instruct<\/code> as its base model and provides an inference engine incorporating parallel constrained decoding.<\/p>\nTraditional autoregressive decoding generates tokens one by one in sequence, which causes latency to increase as schemas grow larger. By evaluating multi-field JSON schemas and choices (enum, boolean) in parallel, this method achieves a 5.6x to 7.0x latency reduction and 100% schema validity on an Apple Silicon M4 Max.<\/p>\nSpecifications<\/h2>\n\nBase Model: Qwen\/Qwen2.5-1.5B-Instruct<\/li>\nParameter Count: 1.54B (Non-embedding parameters: 1.31B)<\/li>\nContext Length: Full 32,768 tokens (up to 8,192 generation tokens)<\/li>\nLicense: apache-2.0<\/li>\n<\/ul>\nPerformance<\/h2>\nAccording to measurements published by the author using mlx-community\/Qwen2.5-1.5B-Instruct-4bit<\/code> on an Apple Silicon M4 Max (macOS Sequoia) environment, the comparison between the standard autoregressive baseline and parallel constrained decoding is as follows:<\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nScenario<\/th>\nFields<\/th>\nAutoregressive Baseline<\/th>\nParallel Constrained<\/th>\nLatency Speedup<\/th>\nSyntax Validity<\/th>\n<\/tr>\n<\/thead>\n\n\nFintech Fraud Routing<\/td>\n4 fields<\/td>\n420 ms (120 tok\/s)<\/td>\n75 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nCode Security Audit<\/td>\n4 fields<\/td>\n380 ms (125 tok\/s)<\/td>\n68 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nHigh-Cardinality Tariff<\/td>\n1 field (255 choices)<\/td>\n500 ms (118 tok\/s)<\/td>\n89 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nEnterprise Support Triage<\/td>\n28 fields<\/td>\n1,900 ms (130 tok\/s)<\/td>\n270 ms<\/td>\n7.0x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe author’s measurements show that the effect of parallel processing becomes more pronounced in scenarios with an increasing number of target fields. For “Enterprise Support Triage,” which extracts 28 fields, conventional autoregressive generation required 1,900 ms, whereas this method achieved a significant speedup of 7.0x at 270 ms.<\/p>\nAdditionally, consistent speedups of 5.6x (75 ms to 89 ms) were recorded for the high-cardinality “High-Cardinality Tariff” with 255 choices, as well as the 4-field “Fintech Fraud Routing” and “Code Security Audit.” In all test cases, syntax validity is 100% guaranteed, and the strength lies in executing high-speed processing while completely preventing grammar errors and missing fields during structured data output.<\/p>\nStrengths and Use Cases<\/h2>\nBuilding upon the high instruction-following and structured data comprehension capabilities of the base model Qwen\/Qwen2.5-1.5B-Instruct<\/code>, this combines parallel constrained decoding technology to exhibit specialized strengths in information extraction and classification tasks.<\/p>\nSpecifically, input context and schema definition text are preloaded (prefilled) into the MLX KV Cache (Key-Value Cache) just once, and that state is broadcast in parallel to the evaluation of each field. By applying mask extraction (logit slicing) only to token IDs permitted for each field to directly calculate probabilities from predefined choices (booleans or up to 255 enums), unnecessary token generation loops are avoided.<\/p>\nThis makes it highly effective in business scenarios such as:\n– Fintech fraud routing (identifying risk levels and determining the necessity of confirmation)\n– Security audits (classifying vulnerable code and assigning remediation priorities)\n– Automated triage of large-scale customer support tickets (department routing and urgency setting)\n– Identifying product codes or tariff classifications (such as HS codes) with numerous classification candidates<\/p>\nSince JSON syntax breaks or unnecessary key outputs (hallucinations) do not occur by design, it is extremely well-suited for integration with backend systems and the construction of automated pipelines where accuracy is required.<\/p>\n<\/p>\nHardware Requirements<\/h2>\nEstimated requirements (calculated by Local Model Watch)<\/strong> \u2014 1.5B parameters (taken from the base model Qwen\/Qwen2.5-1.5B-Instruct)<\/p>\n\n\n\n\nYour VRAM<\/th>\nQuantization<\/th>\nFile size<\/th>\nEst. memory needed<\/th>\n<\/tr>\n<\/thead>\n\n\n4GB (laptop iGPU \/ phone class)<\/td>\nBF16<\/td>\n2.9GB<\/td>\n3.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nMemory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Traditional autoregressive decoding generates tokens one by one in sequence, which causes latency to increase as schemas grow larger. By evaluating multi-field JSON schemas and choices (enum, boolean) in parallel, this method achieves a 5.6x to 7.0x latency reduction and 100% schema validity on an Apple Silicon M4 Max.<\/p>\n
According to measurements published by the author using mlx-community\/Qwen2.5-1.5B-Instruct-4bit<\/code> on an Apple Silicon M4 Max (macOS Sequoia) environment, the comparison between the standard autoregressive baseline and parallel constrained decoding is as follows:<\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nScenario<\/th>\nFields<\/th>\nAutoregressive Baseline<\/th>\nParallel Constrained<\/th>\nLatency Speedup<\/th>\nSyntax Validity<\/th>\n<\/tr>\n<\/thead>\n\n\nFintech Fraud Routing<\/td>\n4 fields<\/td>\n420 ms (120 tok\/s)<\/td>\n75 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nCode Security Audit<\/td>\n4 fields<\/td>\n380 ms (125 tok\/s)<\/td>\n68 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nHigh-Cardinality Tariff<\/td>\n1 field (255 choices)<\/td>\n500 ms (118 tok\/s)<\/td>\n89 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nEnterprise Support Triage<\/td>\n28 fields<\/td>\n1,900 ms (130 tok\/s)<\/td>\n270 ms<\/td>\n7.0x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe author’s measurements show that the effect of parallel processing becomes more pronounced in scenarios with an increasing number of target fields. For “Enterprise Support Triage,” which extracts 28 fields, conventional autoregressive generation required 1,900 ms, whereas this method achieved a significant speedup of 7.0x at 270 ms.<\/p>\nAdditionally, consistent speedups of 5.6x (75 ms to 89 ms) were recorded for the high-cardinality “High-Cardinality Tariff” with 255 choices, as well as the 4-field “Fintech Fraud Routing” and “Code Security Audit.” In all test cases, syntax validity is 100% guaranteed, and the strength lies in executing high-speed processing while completely preventing grammar errors and missing fields during structured data output.<\/p>\nStrengths and Use Cases<\/h2>\nBuilding upon the high instruction-following and structured data comprehension capabilities of the base model Qwen\/Qwen2.5-1.5B-Instruct<\/code>, this combines parallel constrained decoding technology to exhibit specialized strengths in information extraction and classification tasks.<\/p>\nSpecifically, input context and schema definition text are preloaded (prefilled) into the MLX KV Cache (Key-Value Cache) just once, and that state is broadcast in parallel to the evaluation of each field. By applying mask extraction (logit slicing) only to token IDs permitted for each field to directly calculate probabilities from predefined choices (booleans or up to 255 enums), unnecessary token generation loops are avoided.<\/p>\nThis makes it highly effective in business scenarios such as:\n– Fintech fraud routing (identifying risk levels and determining the necessity of confirmation)\n– Security audits (classifying vulnerable code and assigning remediation priorities)\n– Automated triage of large-scale customer support tickets (department routing and urgency setting)\n– Identifying product codes or tariff classifications (such as HS codes) with numerous classification candidates<\/p>\nSince JSON syntax breaks or unnecessary key outputs (hallucinations) do not occur by design, it is extremely well-suited for integration with backend systems and the construction of automated pipelines where accuracy is required.<\/p>\n<\/p>\nHardware Requirements<\/h2>\nEstimated requirements (calculated by Local Model Watch)<\/strong> \u2014 1.5B parameters (taken from the base model Qwen\/Qwen2.5-1.5B-Instruct)<\/p>\n\n\n\n\nYour VRAM<\/th>\nQuantization<\/th>\nFile size<\/th>\nEst. memory needed<\/th>\n<\/tr>\n<\/thead>\n\n\n4GB (laptop iGPU \/ phone class)<\/td>\nBF16<\/td>\n2.9GB<\/td>\n3.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nMemory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
mlx-community\/Qwen2.5-1.5B-Instruct-4bit<\/code> on an Apple Silicon M4 Max (macOS Sequoia) environment, the comparison between the standard autoregressive baseline and parallel constrained decoding is as follows:<\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nScenario<\/th>\nFields<\/th>\nAutoregressive Baseline<\/th>\nParallel Constrained<\/th>\nLatency Speedup<\/th>\nSyntax Validity<\/th>\n<\/tr>\n<\/thead>\n\n\nFintech Fraud Routing<\/td>\n4 fields<\/td>\n420 ms (120 tok\/s)<\/td>\n75 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nCode Security Audit<\/td>\n4 fields<\/td>\n380 ms (125 tok\/s)<\/td>\n68 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nHigh-Cardinality Tariff<\/td>\n1 field (255 choices)<\/td>\n500 ms (118 tok\/s)<\/td>\n89 ms<\/td>\n5.6x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n\nEnterprise Support Triage<\/td>\n28 fields<\/td>\n1,900 ms (130 tok\/s)<\/td>\n270 ms<\/td>\n7.0x<\/td>\n100% guaranteed<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe author’s measurements show that the effect of parallel processing becomes more pronounced in scenarios with an increasing number of target fields. For “Enterprise Support Triage,” which extracts 28 fields, conventional autoregressive generation required 1,900 ms, whereas this method achieved a significant speedup of 7.0x at 270 ms.<\/p>\nAdditionally, consistent speedups of 5.6x (75 ms to 89 ms) were recorded for the high-cardinality “High-Cardinality Tariff” with 255 choices, as well as the 4-field “Fintech Fraud Routing” and “Code Security Audit.” In all test cases, syntax validity is 100% guaranteed, and the strength lies in executing high-speed processing while completely preventing grammar errors and missing fields during structured data output.<\/p>\nStrengths and Use Cases<\/h2>\nBuilding upon the high instruction-following and structured data comprehension capabilities of the base model Qwen\/Qwen2.5-1.5B-Instruct<\/code>, this combines parallel constrained decoding technology to exhibit specialized strengths in information extraction and classification tasks.<\/p>\nSpecifically, input context and schema definition text are preloaded (prefilled) into the MLX KV Cache (Key-Value Cache) just once, and that state is broadcast in parallel to the evaluation of each field. By applying mask extraction (logit slicing) only to token IDs permitted for each field to directly calculate probabilities from predefined choices (booleans or up to 255 enums), unnecessary token generation loops are avoided.<\/p>\nThis makes it highly effective in business scenarios such as:\n– Fintech fraud routing (identifying risk levels and determining the necessity of confirmation)\n– Security audits (classifying vulnerable code and assigning remediation priorities)\n– Automated triage of large-scale customer support tickets (department routing and urgency setting)\n– Identifying product codes or tariff classifications (such as HS codes) with numerous classification candidates<\/p>\nSince JSON syntax breaks or unnecessary key outputs (hallucinations) do not occur by design, it is extremely well-suited for integration with backend systems and the construction of automated pipelines where accuracy is required.<\/p>\n<\/p>\nHardware Requirements<\/h2>\nEstimated requirements (calculated by Local Model Watch)<\/strong> \u2014 1.5B parameters (taken from the base model Qwen\/Qwen2.5-1.5B-Instruct)<\/p>\n\n\n\n\nYour VRAM<\/th>\nQuantization<\/th>\nFile size<\/th>\nEst. memory needed<\/th>\n<\/tr>\n<\/thead>\n\n\n4GB (laptop iGPU \/ phone class)<\/td>\nBF16<\/td>\n2.9GB<\/td>\n3.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nMemory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\u2192 Scroll horizontally to see all columns<\/p>\n
The author’s measurements show that the effect of parallel processing becomes more pronounced in scenarios with an increasing number of target fields. For “Enterprise Support Triage,” which extracts 28 fields, conventional autoregressive generation required 1,900 ms, whereas this method achieved a significant speedup of 7.0x at 270 ms.<\/p>\n
Additionally, consistent speedups of 5.6x (75 ms to 89 ms) were recorded for the high-cardinality “High-Cardinality Tariff” with 255 choices, as well as the 4-field “Fintech Fraud Routing” and “Code Security Audit.” In all test cases, syntax validity is 100% guaranteed, and the strength lies in executing high-speed processing while completely preventing grammar errors and missing fields during structured data output.<\/p>\n
Building upon the high instruction-following and structured data comprehension capabilities of the base model Qwen\/Qwen2.5-1.5B-Instruct<\/code>, this combines parallel constrained decoding technology to exhibit specialized strengths in information extraction and classification tasks.<\/p>\nSpecifically, input context and schema definition text are preloaded (prefilled) into the MLX KV Cache (Key-Value Cache) just once, and that state is broadcast in parallel to the evaluation of each field. By applying mask extraction (logit slicing) only to token IDs permitted for each field to directly calculate probabilities from predefined choices (booleans or up to 255 enums), unnecessary token generation loops are avoided.<\/p>\nThis makes it highly effective in business scenarios such as:\n– Fintech fraud routing (identifying risk levels and determining the necessity of confirmation)\n– Security audits (classifying vulnerable code and assigning remediation priorities)\n– Automated triage of large-scale customer support tickets (department routing and urgency setting)\n– Identifying product codes or tariff classifications (such as HS codes) with numerous classification candidates<\/p>\nSince JSON syntax breaks or unnecessary key outputs (hallucinations) do not occur by design, it is extremely well-suited for integration with backend systems and the construction of automated pipelines where accuracy is required.<\/p>\n<\/p>\nHardware Requirements<\/h2>\nEstimated requirements (calculated by Local Model Watch)<\/strong> \u2014 1.5B parameters (taken from the base model Qwen\/Qwen2.5-1.5B-Instruct)<\/p>\n\n\n\n\nYour VRAM<\/th>\nQuantization<\/th>\nFile size<\/th>\nEst. memory needed<\/th>\n<\/tr>\n<\/thead>\n\n\n4GB (laptop iGPU \/ phone class)<\/td>\nBF16<\/td>\n2.9GB<\/td>\n3.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nMemory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Qwen\/Qwen2.5-1.5B-Instruct<\/code>, this combines parallel constrained decoding technology to exhibit specialized strengths in information extraction and classification tasks.<\/p>\nSpecifically, input context and schema definition text are preloaded (prefilled) into the MLX KV Cache (Key-Value Cache) just once, and that state is broadcast in parallel to the evaluation of each field. By applying mask extraction (logit slicing) only to token IDs permitted for each field to directly calculate probabilities from predefined choices (booleans or up to 255 enums), unnecessary token generation loops are avoided.<\/p>\nThis makes it highly effective in business scenarios such as:\n– Fintech fraud routing (identifying risk levels and determining the necessity of confirmation)\n– Security audits (classifying vulnerable code and assigning remediation priorities)\n– Automated triage of large-scale customer support tickets (department routing and urgency setting)\n– Identifying product codes or tariff classifications (such as HS codes) with numerous classification candidates<\/p>\nSince JSON syntax breaks or unnecessary key outputs (hallucinations) do not occur by design, it is extremely well-suited for integration with backend systems and the construction of automated pipelines where accuracy is required.<\/p>\n<\/p>\nHardware Requirements<\/h2>\nEstimated requirements (calculated by Local Model Watch)<\/strong> \u2014 1.5B parameters (taken from the base model Qwen\/Qwen2.5-1.5B-Instruct)<\/p>\n\n\n\n\nYour VRAM<\/th>\nQuantization<\/th>\nFile size<\/th>\nEst. memory needed<\/th>\n<\/tr>\n<\/thead>\n\n\n4GB (laptop iGPU \/ phone class)<\/td>\nBF16<\/td>\n2.9GB<\/td>\n3.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nMemory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Specifically, input context and schema definition text are preloaded (prefilled) into the MLX KV Cache (Key-Value Cache) just once, and that state is broadcast in parallel to the evaluation of each field. By applying mask extraction (logit slicing) only to token IDs permitted for each field to directly calculate probabilities from predefined choices (booleans or up to 255 enums), unnecessary token generation loops are avoided.<\/p>\n
This makes it highly effective in business scenarios such as:\n– Fintech fraud routing (identifying risk levels and determining the necessity of confirmation)\n– Security audits (classifying vulnerable code and assigning remediation priorities)\n– Automated triage of large-scale customer support tickets (department routing and urgency setting)\n– Identifying product codes or tariff classifications (such as HS codes) with numerous classification candidates<\/p>\n
Since JSON syntax breaks or unnecessary key outputs (hallucinations) do not occur by design, it is extremely well-suited for integration with backend systems and the construction of automated pipelines where accuracy is required.<\/p>\n
Estimated requirements (calculated by Local Model Watch)<\/strong> \u2014 1.5B parameters (taken from the base model Qwen\/Qwen2.5-1.5B-Instruct)<\/p>\n\n\n\n\nYour VRAM<\/th>\nQuantization<\/th>\nFile size<\/th>\nEst. memory needed<\/th>\n<\/tr>\n<\/thead>\n\n\n4GB (laptop iGPU \/ phone class)<\/td>\nBF16<\/td>\n2.9GB<\/td>\n3.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nMemory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference<\/a>. What the quantization names mean: glossary<\/a>.<\/em><\/p>\n<\/p>\n<\/p>\nCan You Run It Locally?<\/h2>\nNot usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Not usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\nThe publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\nLicense \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
The publisher ships MLX only. Today it can be run with MLX (Apple Silicon), using the memory figures in the table above.<\/p>\n
License \u2014 apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\nCompiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Compiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found” means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher’s original terms before relying on them.<\/em><\/p>\n<\/p>\n<\/p>\nOur Own Measurements<\/h2>\nValues we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Values we measured ourselves on our server (no GPU) by actually reading and running this model’s files \u2014 not figures copied from the model card. How we measure, and the results for every model: Our Measurements<\/a>.<\/p>\nJapanese Token Efficiency<\/h3>\n\n\n\n\nTokenizer<\/th>\nTokens per 1,000 Japanese characters<\/th>\nRatio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n\n\nThis model<\/strong><\/td>\n688<\/strong><\/td>\n1.26\u00d7<\/strong><\/td>\n<\/tr>\n\nQwen3<\/td>\n688<\/td>\n1.26\u00d7<\/td>\n<\/tr>\n\nLlama 3.2<\/td>\n744<\/td>\n1.36\u00d7<\/td>\n<\/tr>\n\nGemma 3<\/td>\n564<\/td>\n1.03\u00d7<\/td>\n<\/tr>\n\ngpt-oss<\/td>\n795<\/td>\n1.45\u00d7<\/td>\n<\/tr>\n\nLLM-jp-3<\/td>\n497<\/td>\n0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\nThe same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\nCounted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
The same Japanese text takes about as many tokens as with the Qwen3 tokenizer.<\/p>\n
Counted with the tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
tokenizer.json<\/code> of Qwen\/Qwen2.5-1.5B-Instruct<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<\/p>\n<\/p>\nRecent Models in the Same Size Class<\/h2>\nModels with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Models with <\/em>up to 4B<\/em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.<\/em><\/p>\n\n\u2192 Scroll horizontally to see all columns<\/p>\n\n\n\nModel<\/th>\nParameters<\/th>\nSmallest VRAM tier<\/th>\nLicense<\/th>\nOur article<\/th>\n<\/tr>\n<\/thead>\n\n\nLiquidAI\/LFM2.5-350M-Diffusion-Exp<\/td>\n425M<\/td>\n4GB<\/td>\nlfm1.0<\/td>\nLFM2.5-350M-Diffusion-Exp Text Generation Model: 4GB+ VRAM<\/a> (2026-10-03)<\/td>\n<\/tr>\n\ntogethercomputer\/Tev1-0.8B-experimental<\/td>\n873M<\/td>\n4GB<\/td>\n\u2014<\/td>\nTev1-0.8B-experimental Text Generation Model: 4GB+ VRAM<\/a> (2026-09-25)<\/td>\n<\/tr>\n\npfnet\/plamo-3-610m-fin-instruct<\/td>\n890M<\/td>\n4GB<\/td>\nplamo-community-license<\/td>\nplamo-3-610m-fin-instruct Text Generation Model: 4GB+ VRAM<\/a> (2026-09-24)<\/td>\n<\/tr>\n\ntencent\/Simple-Attention-Sparsification<\/td>\n4.0B<\/td>\n12GB<\/td>\n\u2014<\/td>\nSimple-Attention-Sparsification Text Generation Model: 12GB+ VRAM<\/a> (2026-09-14)<\/td>\n<\/tr>\n\nopenbmb\/MiniCPM5-2B<\/td>\n2.5B<\/td>\n4GB<\/td>\napache-2.0<\/td>\nMiniCPM5-2B Text Generation Model: Our Test Answers, 4GB+ VRAM<\/a> (2026-09-07)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/p>\nHow to Get It<\/h2>\nThis model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\ngit clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
This model and inference engine are available through the MLX library. Setup procedures and dependency installations can be executed with the following commands:<\/p>\n
git clone https:\/\/github.com\/your-org\/parallel-constrained-decoding.git\ncd parallel-constrained-decoding\n\npython3 -m venv.venv\nsource.venv\/bin\/activate\npip install -r requirements.txt\n<\/code><\/pre>\nFrom Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
From Python code, you can use StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
StructuredSchema<\/code> to define schemas and call the run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
run_parallel_generation<\/code> function to execute parallel high-speed inference. You can also launch the web interface (python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000<\/code>) to check latency comparisons with conventional autoregressive generation and structured data output right in your browser.<\/p>\n<\/p>\nOther Models for the Same Task<\/h2>\nRecent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Recent text generation models covered by Local Model Watch, newest first. Grouped by the task each publisher declares on Hugging Face (pipeline_tag); the smallest VRAM tier is this site’s estimate.<\/em><\/p>\n\nNaive-N0.5-Flash MoE Model for AI Research and Coding: ~690GB Memory<\/a> (Over 80GB)<\/li>\nELYZA Releases ELYZA-Thinking-1.0 32B\/33B Reasoning Models<\/a> (32.1B, 80GB)<\/li>\nOrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a> (27.8B, 16GB)<\/li>\nHemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a> (26.9B, 12GB)<\/li>\n<\/ul>\nSee all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
See all text generation models \u2192<\/a><\/p>\n<\/p>\n<\/p>\nWhat to Read Next<\/h2>\n\nFind models by VRAM<\/strong> (This model runs from the 4GB tier) \u2192 Other models that run on a 8GB GPU<\/a><\/li>\nFormats this model is available in<\/strong> \u2192 MLX format guide and models<\/a><\/li>\nLearn about the publisher<\/strong> \u2192 Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<\/p>\nSources<\/h2>\n\nhttps:\/\/huggingface.co\/harshatheg\/Qwen-2.5-1B-RLCD<\/a><\/li>\n<\/ul>\n<\/p>\nUpdate History<\/h2>\n\n2026-09-24: Rebuilt the article: Added a “can you run it locally?” verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n2026-09-28: Added our own measurements: Japanese token efficiency.<\/li>\n<\/ul>\n<\/p>\n","protected":false},"excerpt":{"rendered":"Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/p>\n","protected":false},"excerpt":{"rendered":"
Explore harshatheg\/Qwen-2.5-1B-RLCD, an MLX implementation using parallel constrained decoding on Apple Silicon for high-speed, structured JSON output.<\/p>\n","protected":false},"author":1,"featured_media":4022,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[505,1386,977,1388,1390,117],"class_list":["post-1038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-apple-silicon-en","tag-harshatheg-qwen-2-5-1b-rlcd-en","tag-mlx-en","tag-qwen2-5-en","tag--en"],"lang":"en","translations":{"en":1038,"ja":1036},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=1038"}],"version-history":[{"count":36,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions"}],"predecessor-version":[{"id":9988,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/1038\/revisions\/9988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4022"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=1038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=1038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=1038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}