{"id":672,"date":"2026-09-15T20:16:56","date_gmt":"2026-09-15T11:16:56","guid":{"rendered":"https:\/\/localmodelwatch.tsuchitsuchi.com\/2026\/09\/15\/salience-27b-r6-gguf-2\/"},"modified":"2026-10-30T11:08:28","modified_gmt":"2026-10-30T02:08:28","slug":"salience-27b-r6-gguf-2","status":"publish","type":"post","link":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/15\/salience-27b-r6-gguf-2\/","title":{"rendered":"vectionlabs_Salience-27B-R6-GGUF: Our Test Answers, 12GB+ VRAM"},"content":{"rendered":"<p><!-- lmw:facts --><\/p>\n<h2>At a Glance<\/h2>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Repository<\/td>\n<td><a href=\"https:\/\/huggingface.co\/bartowski\/vectionlabs_Salience-27B-R6-GGUF\">bartowski\/vectionlabs_Salience-27B-R6-GGUF<\/a><\/td>\n<\/tr>\n<tr>\n<td>Family guide<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/model-qwen-qwen3-8-en\/\">Qwen3.8 guide (8 articles)<\/a><\/td>\n<\/tr>\n<tr>\n<td>Publisher guide<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/publisher-alibaba-en\/\">Alibaba (Qwen): models and licenses<\/a><\/td>\n<\/tr>\n<tr>\n<td>Published<\/td>\n<td>2026-09-14<\/td>\n<\/tr>\n<tr>\n<td>License<\/td>\n<td>apache-2.0<\/td>\n<\/tr>\n<tr>\n<td>Formats<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/format-gguf-en\/\">GGUF<\/a><\/td>\n<\/tr>\n<tr>\n<td>Source type<\/td>\n<td>Primary source (the publisher itself)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><em>Values determined by this site&#8217;s code when the information was collected. Dates are JST.<\/em><\/p>\n<p><!-- \/lmw:facts --><\/p>\n<p><!-- lmw:lab-summary --><\/p>\n<p><strong>What we checked ourselves<\/strong><\/p>\n<ul>\n<li>Ran <code>vectionlabs_Salience-27B-R6-Q3_K_M.gguf<\/code> (Q3_K_M) on our CPU-only server (Neoverse-N1, 3 threads, llama.cpp b11223): generation at 0.9 tokens\/s (a CPU figure, not a GPU speed).<\/li>\n<li>Its answers to our fixed Japanese questions are shown verbatim below.<\/li>\n<li>It needs about 21% fewer tokens than the Qwen3 tokenizer for the same Japanese text, so about 1.26\u00d7 as much Japanese fits in the same context length, and generation is faster per character.<\/li>\n<\/ul>\n<p>Details and conditions are in \u201cOur Own Measurements\u201d below.<\/p>\n<p><!-- \/lmw:lab-summary --><\/p>\n<p>bartowski has released the GGUF quantized version of &#8220;Salience-27B-R6&#8221;, developed by Vection Labs, as <code>bartowski\/vectionlabs_Salience-27B-R6-GGUF<\/code>.<\/p>\n<p>This model is a full-parameter active 27B Dense model based on Qwen3.8, supporting text as well as image and video inputs. It implements &#8220;Reasoning economy&#8221; at the weight level to suppress excessive consumption of thinking tokens while maintaining thinking capabilities, aiming for improved efficiency particularly in long-term agent loop processing.<\/p>\n<h2>Specifications<\/h2>\n<ul>\n<li>Parameters: 27.8B (Dense)<\/li>\n<li>Context Length: 1,048,576 tokens (YaRN + Dual Chunk Attention)<\/li>\n<li>License: apache-2.0<\/li>\n<\/ul>\n<h2>Performance<\/h2>\n<p>According to the original model card, measurement results from official benchmark suites have not been published (unmeasured).<\/p>\n<p>On the other hand, as part of the model&#8217;s design policy, optimizations have been implemented to intentionally shorten the reasoning chain required to reach the same conclusion compared to the previous version (R5). This is reported to significantly reduce the overall task completion time in agent workloads involving multi-step tool calls. However, it is explained that as a trade-off for shortening the reasoning, scores on multiple-choice knowledge benchmarks and the like may drop slightly compared to the previous version.<\/p>\n<h2>Strengths and Use Cases<\/h2>\n<ul>\n<li>Software engineering and coding agents (code generation, debugging, repository-wide changes, etc.).<\/li>\n<li>Terminal operation and tool integration (multi-step command planning, execution result verification, error recovery).<\/li>\n<li>Technical analysis leveraging multimodal inputs (reading architecture diagrams, UI screenshots, stack trace images, whiteboard photos, etc.).<\/li>\n<li>Built-in MTP (Multi-Token Prediction) head, enabling fast inference via self-speculative decoding in compatible environments.<\/li>\n<\/ul>\n<h2>Differences from Similar Models<\/h2>\n<p>Other articles covering derivatives of the same base model (Qwen3.8-27B) include <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/2026\/09\/12\/signal-3-8-27b-gguf\/\">Qwen3.8-27B Fast Fine-tune &#8220;Signal-3.8-27B-GGUF&#8221; Released<\/a>, which aims to reduce token count and improve response speed, and <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/2026\/09\/06\/qwopus3-8-27b-flash-gguf\/\">Qwopus3.8-27B-Flash GGUF Released, Lightweight Model for Agents<\/a>, which features lightweight optimizations for agents.<\/p>\n<p>In contrast, Salience-27B-R6 differs by adopting a 27B Dense configuration where all parameters operate constantly rather than an MoE configuration, building short-thinking optimization directly into the model weights rather than through configuration changes, and providing native support for multimodal inputs (images and video) and ultra-long contexts exceeding 1 million tokens.<\/p>\n<p><!-- lmw:hardware --><\/p>\n<h2>Hardware Requirements<\/h2>\n<p><strong>Estimated requirements (calculated by Local Model Watch)<\/strong> \u2014 27.8B parameters (taken from the base model vectionlabs\/Salience-27B-R6)<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Your VRAM<\/th>\n<th>Quantization<\/th>\n<th>File size<\/th>\n<th>Est. memory needed<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>12GB (RTX 4070 \/ 3060 12GB, etc.)<\/td>\n<td>IQ2_M<\/td>\n<td>9.8GB<\/td>\n<td>11.8GB<\/td>\n<\/tr>\n<tr>\n<td>16GB (RTX 5060 Ti 16GB \/ 4060 Ti 16GB, etc.)<\/td>\n<td>Q3_K_L<\/td>\n<td>13.2GB<\/td>\n<td>15.8GB<\/td>\n<\/tr>\n<tr>\n<td>24GB (RTX 4090 \/ 3090, etc.)<\/td>\n<td>Q5_K_M<\/td>\n<td>19.5GB<\/td>\n<td>23.4GB<\/td>\n<\/tr>\n<tr>\n<td>32GB (RTX 5090, etc.)<\/td>\n<td>Q6_K_L<\/td>\n<td>23.2GB<\/td>\n<td>27.9GB<\/td>\n<\/tr>\n<tr>\n<td>48GB (RTX 6000 Ada \/ A6000, etc.)<\/td>\n<td>Q8_0<\/td>\n<td>27.1GB<\/td>\n<td>32.5GB<\/td>\n<\/tr>\n<tr>\n<td>80GB class (A100 \/ H100)<\/td>\n<td>BF16<\/td>\n<td>50.9GB<\/td>\n<td>61.1GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><em>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&#8217;s authors. Compare with other models in our <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/vram-guide-en\/\">VRAM quick reference<\/a>. What the quantization names mean: <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/glossary-quantization-en\/\">glossary<\/a>.<\/em><\/p>\n<p><!-- \/lmw:hardware --><\/p>\n<p><!-- lmw:runnability --><\/p>\n<h2>Can You Run It Locally?<\/h2>\n<p><strong>Runs in Ollama, LM Studio and llama.cpp as-is.<\/strong><\/p>\n<p>It is distributed in GGUF, so no conversion is needed.<\/p>\n<p><strong>License \u2014 <code>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>\n<p><strong>Compression:<\/strong> the IQ2_M build measures 3.03 bits per weight \u2014 about 19% the size of the original 16-bit weights, calculated by this site from the actual file sizes.<\/p>\n<p><em>Compiled by this site&#8217;s code from the published formats, converted builds we have found, and each engine&#8217;s own model registry. &#8220;Not found&#8221; means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher&#8217;s original terms before relying on them.<\/em><\/p>\n<p><!-- \/lmw:runnability --><\/p>\n<p><!-- lmw:lab --><\/p>\n<h2>Our Own Measurements<\/h2>\n<p>Values we measured ourselves on our server (no GPU) by actually reading and running this model&#8217;s files \u2014 not figures copied from the model card. How we measure, and the results for every model: <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/observations-en\/\">Our Measurements<\/a>.<\/p>\n<h3>What We Found<\/h3>\n<p>When running bartowski&#8217;s vectionlabs_Salience-27B on a CPU-only environment, it demonstrated good capability in specific tasks such as Japanese token efficiency, text summarization, and shopping word problems, while also revealing weaknesses such as slow generation speeds and errors in knowledge-based questions. In an environment with a Neoverse-N1 processor and 3 threads using the Q3_K_M quantized model, the generation speed was 0.87 tokens per second, which translates to 1.6 Japanese characters per second, making it significantly slower compared to the 4.57 tokens per second of Qwen3-8B measured under the same conditions. Additionally, because the 4-bit version was too large for the memory capacity, measurements had to be taken using a lower bit quantization.<\/p>\n<p>Regarding Japanese token efficiency, the number of tokens per 1,000 Japanese characters was 546, which is lower than the 688 tokens of the comparison target Qwen3, allowing longer Japanese text to fit within the same context length. In the evaluation using five Japanese questions, the task to summarize an article of approximately 1,200 characters into three sentences successfully produced a 166-character response with exactly three sentences as instructed, yielding a clean result without truncation at the upper limit or repetition. It also correctly answered 280 yen in the shopping word problem, and generated answers all the way through without truncation for creating three spring haiku and polite business emails.<\/p>\n<p>However, in the knowledge question asking for prefectures not facing the ocean, while it listed 4 out of the 8 prefectures that do not face the ocean, it also incorrectly included 2 prefectures that actually do face the ocean. Minor inconsistencies were also observed, such as four English words getting mixed into the response for the polite email. The prompt reading speed was recorded at 1.04 tokens per second with a peak memory usage of 14.7 GB, meaning that both processing speed and factual accuracy need to be carefully evaluated for practical use.<\/p>\n<h3>Japanese Token Efficiency<\/h3>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Tokenizer<\/th>\n<th>Tokens per 1,000 Japanese characters<\/th>\n<th>Ratio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>This model<\/strong><\/td>\n<td><strong>546<\/strong><\/td>\n<td><strong>0.99\u00d7<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Qwen3<\/td>\n<td>688<\/td>\n<td>1.26\u00d7<\/td>\n<\/tr>\n<tr>\n<td>Llama 3.2<\/td>\n<td>744<\/td>\n<td>1.36\u00d7<\/td>\n<\/tr>\n<tr>\n<td>Gemma 3<\/td>\n<td>564<\/td>\n<td>1.03\u00d7<\/td>\n<\/tr>\n<tr>\n<td>gpt-oss<\/td>\n<td>795<\/td>\n<td>1.45\u00d7<\/td>\n<\/tr>\n<tr>\n<td>LLM-jp-3<\/td>\n<td>497<\/td>\n<td>0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>It needs about 21% fewer tokens than the Qwen3 tokenizer for the same Japanese text, so about 1.26\u00d7 as much Japanese fits in the same context length, and generation is faster per character.<\/p>\n<p>Counted with the <code>tokenizer.json<\/code> of <a href=\"https:\/\/huggingface.co\/vectionlabs\/Salience-27B-R6\">vectionlabs\/Salience-27B-R6<\/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<h3>Inside the GGUF File<\/h3>\n<p>File: <a href=\"https:\/\/huggingface.co\/bartowski\/vectionlabs_Salience-27B-R6-GGUF\/blob\/main\/vectionlabs_Salience-27B-R6-Q4_K_M.gguf\">vectionlabs_Salience-27B-R6-Q4_K_M.gguf<\/a> (16.24GB, <code>Q4_K_M<\/code>). We read only the header (metadata) of the file, not the weights.<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Architecture (as named in the GGUF)<\/td>\n<td><code>qwen35<\/code><\/td>\n<\/tr>\n<tr>\n<td>Maximum trained context length<\/td>\n<td>1,048,576 tokens<\/td>\n<\/tr>\n<tr>\n<td>Layers<\/td>\n<td>65<\/td>\n<\/tr>\n<tr>\n<td>Vocabulary size<\/td>\n<td>248,320<\/td>\n<\/tr>\n<tr>\n<td>Chat template<\/td>\n<td>Included (mentions tool calls, has a thinking switch)<\/td>\n<\/tr>\n<tr>\n<td>imatrix<\/td>\n<td>Used (<code>vectionlabs_Salience-27B-R6-calibration-v6.txt<\/code>, 624 chunks)<\/td>\n<\/tr>\n<tr>\n<td>Weight types (share of parameters)<\/td>\n<td>Q4_K 74.5% \/ Q6_K 18.4% \/ Q8_0 4.8% \/ Q4_0 1.6% \/ other 0.7%<\/td>\n<\/tr>\n<tr>\n<td>Average bits per weight<\/td>\n<td>5.10 bits<\/td>\n<\/tr>\n<tr>\n<td>Embedding \/ output layer type<\/td>\n<td>Q4_K \/ Q6_K<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The quant name in the file name describes the file as a whole; in practice layers mix several types. The average bits per weight is the measured file data divided by the number of weights.<\/p>\n<h3>Running It on a CPU Only<\/h3>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Result<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Prompt processing (512 tokens)<\/td>\n<td>1.0 tokens\/s<\/td>\n<\/tr>\n<tr>\n<td>Generation (128 tokens)<\/td>\n<td>0.9 tokens\/s<\/td>\n<\/tr>\n<tr>\n<td>Generation in Japanese characters<\/td>\n<td>about 1.6 characters\/s<\/td>\n<\/tr>\n<tr>\n<td>Peak memory (context 4,096 tokens)<\/td>\n<td>14.7GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Conditions: Neoverse-N1, 3 threads, no GPU, llama.cpp b11223, <code>Q3_K_M<\/code> (12.48GB) from <a href=\"https:\/\/huggingface.co\/bartowski\/vectionlabs_Salience-27B-R6-GGUF\">bartowski\/vectionlabs_Salience-27B-R6-GGUF<\/a>, measured 2026-09-29 (JST). The file plus the part that reads images would exceed our 13GB limit, so we skipped the image questions and asked only the text questions. The 4-bit files did not fit our 13GB limit, so we measured the lower-bit <code>Q3_K_M<\/code> file; quality is lower than at 4 bits. Other models measured under the same conditions (closest in file size):<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<p class=\"lmw-table-hint\" style=\"margin:0 0 4px;font-size:0.85em;opacity:0.7;\">\u2192 Scroll horizontally to see all columns<\/p>\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Model<\/th>\n<th>Quant<\/th>\n<th>File size<\/th>\n<th>Generation<\/th>\n<th>In Japanese<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Qwopus3.8-27B-Flash<\/td>\n<td><code>Q3_K_M<\/code><\/td>\n<td>12.57GB<\/td>\n<td>0.9 tokens\/s<\/td>\n<td>1.7 chars\/s<\/td>\n<\/tr>\n<tr>\n<td>K2-Horizon-MoVA-36B-A4B<\/td>\n<td><code>Q2_K<\/code><\/td>\n<td>12.64GB<\/td>\n<td>5.0 tokens\/s<\/td>\n<td>8.9 chars\/s<\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/11\/pantheon-reasoning-26b-gguf\/\">Gryphe_Pantheon-Reasoning-26B-A4B-1.1-V2<\/a><\/td>\n<td><code>Q3_K_S<\/code><\/td>\n<td>12.26GB<\/td>\n<td>6.2 tokens\/s<\/td>\n<td>11.0 chars\/s<\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/09\/nex-n25-mini-released\/\">nex-agi_Nex-N2.5-mini<\/a><\/td>\n<td><code>Q2_K<\/code><\/td>\n<td>12.89GB<\/td>\n<td>7.3 tokens\/s<\/td>\n<td>13.4 chars\/s<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Peak memory includes the model file mapped into memory (mmap). A PC with a GPU will be much faster; these figures are a guide for CPU-only machines such as mini PCs and ARM boards.<\/p>\n<h3>Answers to Our Japanese Questions (Verbatim)<\/h3>\n<p>We ask every model the same five questions in Japanese under the conditions above (temperature 0, thinking turned off where the chat template allows it, up to 1024 tokens) and publish the answers unedited. We do not grade them; where a question has a single correct answer, we show it for comparison.<\/p>\n<p><strong>Summarization<\/strong><\/p>\n<blockquote>\n<p>\u6b21\u306e\u8a18\u4e8b\u30923\u6587\u3067\u8981\u7d04\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<p>\u307f\u3069\u308a\u5e02\u306f\u4eca\u5e74\u306e\u6625\u3001\u5e02\u5185\u306b\u4e09\u3064\u3042\u308b\u516c\u7acb\u56f3\u66f8\u9928\u306e\u3046\u3061\u6700\u3082\u53e4\u3044\u4e2d\u592e\u56f3\u66f8\u9928\u306e\u6539\u4fee\u3092\u7d42\u3048\u3001\u5341\u6708\u4e00\u65e5\u306b\u518d\u958b\u9928\u3057\u305f\u3002\u6539\u4fee\u306f\u7d04\u4e00\u5e74\u534a\u306b\u308f\u305f\u308a\u3001\u7dcf\u4e8b\u696d\u8cbb\u306f\u7d04\u5341\u4e8c\u5104\u5186\u3002\u3053\u306e\u3046\u3061\u7d04\u56db\u5104\u5186\u306f\u56fd\u306e\u88dc\u52a9\u91d1\u3067\u307e\u304b\u306a\u308f\u308c\u305f\u3002<\/p>\n<p>\u6700\u5927\u306e\u5909\u5316\u306f\u3001\u95b2\u89a7\u5e2d\u306e\u914d\u7f6e\u3067\u3042\u308b\u3002\u4ee5\u524d\u306f\u9928\u5185\u306e\u5ea7\u5e2d\u306e\u5927\u534a\u304c\u5b66\u7fd2\u7528\u306e\u673a\u3067\u3001\u8a66\u9a13\u671f\u9593\u306b\u306a\u308b\u3068\u4e2d\u9ad8\u751f\u3067\u57cb\u307e\u308a\u3001\u9ad8\u9f62\u306e\u5229\u7528\u8005\u3084\u5c0f\u3055\u306a\u5b50\u3069\u3082\u9023\u308c\u304c\u5ea7\u308c\u306a\u3044\u3068\u3044\u3046\u82e6\u60c5\u304c\u6bce\u5e74\u5bc4\u305b\u3089\u308c\u3066\u3044\u305f\u3002\u6539\u4fee\u5f8c\u306f\u5ea7\u5e2d\u6570\u3092\u767e\u4e8c\u5341\u5e2d\u304b\u3089\u4e8c\u767e\u5e2d\u306b\u5897\u3084\u3057\u305f\u3046\u3048\u3067\u3001\u5b66\u7fd2\u7528\u306e\u673a\u3092\u4e09\u968e\u306b\u307e\u3068\u3081\u3001\u4e00\u968e\u306f\u4f1a\u8a71\u304c\u3067\u304d\u308b\u300c\u306b\u304e\u308f\u3044\u30d5\u30ed\u30a2\u300d\u3001\u4e8c\u968e\u306f\u9759\u304b\u306b\u672c\u3092\u8aad\u3080\u300c\u95b2\u89a7\u30d5\u30ed\u30a2\u300d\u306b\u5206\u3051\u305f\u3002\u4e00\u968e\u306b\u306f\u4e88\u7d04\u5236\u306e\u30b0\u30eb\u30fc\u30d7\u5b66\u7fd2\u5ba4\u304c\u56db\u5ba4\u3068\u3001\u98f2\u307f\u7269\u3092\u6301\u3061\u8fbc\u3081\u308b\u30ab\u30d5\u30a7\u30b9\u30da\u30fc\u30b9\u3082\u8a2d\u3051\u3089\u308c\u305f\u3002<\/p>\n<p>\u8535\u66f8\u306e\u9762\u3067\u3082\u898b\u76f4\u3057\u304c\u9032\u3093\u3060\u3002\u8cb8\u51fa\u306e\u5c11\u306a\u3044\u53e4\u3044\u8cc7\u6599\u7d04\u4e09\u4e07\u518a\u3092\u5e02\u5185\u306e\u4ed6\u306e\u9928\u3084\u4fdd\u5b58\u66f8\u5eab\u306b\u79fb\u3057\u3001\u4ee3\u308f\u308a\u306b\u5b50\u3069\u3082\u5411\u3051\u306e\u7d75\u672c\u3068\u3001\u4ecb\u8b77\u30fb\u5b50\u80b2\u3066\u30fb\u4ed5\u4e8b\u63a2\u3057\u3068\u3044\u3063\u305f\u66ae\u3089\u3057\u306b\u95a2\u308f\u308b\u5b9f\u7528\u66f8\u3092\u7d04\u4e00\u4e07\u4e94\u5343\u518a\u5897\u3084\u3057\u305f\u3002\u96fb\u5b50\u66f8\u7c4d\u306e\u8cb8\u51fa\u30b5\u30fc\u30d3\u30b9\u3082\u59cb\u307e\u308a\u3001\u5229\u7528\u767b\u9332\u3092\u3059\u308c\u3070\u81ea\u5b85\u304b\u3089\u4e00\u5ea6\u306b\u4e09\u518a\u307e\u3067\u3001\u4e8c\u9031\u9593\u501f\u308a\u3089\u308c\u308b\u3002<\/p>\n<p>\u5efa\u7269\u305d\u306e\u3082\u306e\u306b\u3082\u624b\u304c\u5165\u3063\u305f\u3002\u7bc9\u56db\u5341\u5e74\u3092\u8d85\u3048\u3066\u3044\u305f\u7a7a\u8abf\u8a2d\u5099\u3092\u5165\u308c\u66ff\u3048\u3001\u5c4b\u4e0a\u306b\u306f\u592a\u967d\u5149\u30d1\u30cd\u30eb\u3092\u8a2d\u7f6e\u3057\u3066\u3001\u9928\u5185\u3067\u4f7f\u3046\u96fb\u529b\u306e\u7d04\u4e8c\u5272\u3092\u307e\u304b\u306a\u3048\u308b\u3088\u3046\u306b\u3057\u305f\u3002\u8eca\u3044\u3059\u306e\u5229\u7528\u8005\u304b\u3089\u8981\u671b\u306e\u591a\u304b\u3063\u305f\u30a8\u30ec\u30d9\u30fc\u30bf\u30fc\u306f\u4e00\u57fa\u304b\u3089\u4e8c\u57fa\u306b\u5897\u3048\u3001\u66f8\u67b6\u306e\u9593\u306e\u901a\u8def\u3082\u4ee5\u524d\u3088\u308a\u4e09\u5341\u30bb\u30f3\u30c1\u5e83\u3052\u3089\u308c\u305f\u3002\u5165\u53e3\u306b\u306f\u6bb5\u5dee\u306e\u306a\u3044\u30b9\u30ed\u30fc\u30d7\u304c\u65b0\u305f\u306b\u8a2d\u3051\u3089\u308c\u3001\u6388\u4e73\u5ba4\u3068\u591a\u76ee\u7684\u30c8\u30a4\u30ec\u3082\u5404\u968e\u306b\u4e00\u3064\u305a\u3064\u7f6e\u304b\u308c\u305f\u3002<\/p>\n<p>\u4e00\u65b9\u3067\u8ab2\u984c\u3082\u6b8b\u308b\u3002\u99d0\u8eca\u5834\u306f\u6539\u4fee\u524d\u3068\u540c\u3058\u56db\u5341\u53f0\u306e\u307e\u307e\u3067\u3001\u9031\u672b\u306b\u306f\u8fd1\u304f\u306e\u9053\u8def\u306b\u9806\u756a\u5f85\u3061\u306e\u8eca\u304c\u4e26\u3093\u3060\u3002\u5e02\u306f\u96a3\u63a5\u3059\u308b\u5e02\u6709\u5730\u3092\u81e8\u6642\u99d0\u8eca\u5834\u3068\u3057\u3066\u958b\u653e\u3057\u305f\u304c\u3001\u6052\u4e45\u7684\u306a\u5bfe\u7b56\u306f\u6c7a\u307e\u3063\u3066\u3044\u306a\u3044\u3002\u307e\u305f\u3001\u958b\u9928\u6642\u9593\u3092\u5e73\u65e5\u306e\u591c\u4e5d\u6642\u307e\u3067\u5ef6\u3070\u3057\u305f\u3053\u3068\u3067\u8077\u54e1\u306e\u8ca0\u62c5\u304c\u5897\u3048\u3066\u304a\u308a\u3001\u8077\u54e1\u56e3\u4f53\u306f\u4eba\u54e1\u306e\u8ffd\u52a0\u3092\u6c42\u3081\u3066\u3044\u308b\u3002<\/p>\n<p>\u5229\u7528\u8005\u306e\u53d7\u3051\u6b62\u3081\u306f\u304a\u304a\u3080\u306d\u597d\u610f\u7684\u3060\u3002\u9031\u306b\u4e8c\u5ea6\u3001\u5b6b\u3092\u9023\u308c\u3066\u6765\u308b\u3068\u3044\u3046\u4e03\u5341\u4ee3\u306e\u5973\u6027\u306f\u300c\u524d\u306f\u5b50\u3069\u3082\u304c\u58f0\u3092\u51fa\u3059\u3068\u5468\u308a\u306e\u76ee\u304c\u6c17\u306b\u306a\u3063\u305f\u304c\u3001\u4e00\u968e\u306a\u3089\u6c17\u517c\u306d\u306a\u304f\u7d75\u672c\u3092\u8aad\u3093\u3067\u3042\u3052\u3089\u308c\u308b\u300d\u3068\u8a71\u3059\u3002\u4e00\u65b9\u3001\u53d7\u9a13\u3092\u63a7\u3048\u305f\u9ad8\u6821\u751f\u306e\u4e2d\u306b\u306f\u300c\u5b66\u7fd2\u5e2d\u304c\u4e09\u968e\u3060\u3051\u306b\u306a\u308a\u3001\u5915\u65b9\u306f\u3059\u3050\u306b\u6e80\u5e2d\u306b\u306a\u308b\u300d\u3068\u4e0d\u6e80\u3092\u6f0f\u3089\u3059\u4eba\u3082\u3044\u308b\u3002\u56f3\u66f8\u9928\u306f\u3001\u5b66\u7fd2\u5e2d\u306e\u7a7a\u304d\u72b6\u6cc1\u3092\u30db\u30fc\u30e0\u30da\u30fc\u30b8\u3067\u78ba\u8a8d\u3067\u304d\u308b\u4ed5\u7d44\u307f\u3092\u5e74\u5185\u306b\u5c0e\u5165\u3059\u308b\u4e88\u5b9a\u3060\u3002<\/p>\n<p>\u518d\u958b\u9928\u306b\u5408\u308f\u305b\u3066\u3001\u50ac\u3057\u3082\u5897\u3048\u305f\u3002\u6bce\u9031\u571f\u66dc\u306e\u5348\u524d\u306b\u306f\u8aad\u307f\u805e\u304b\u305b\u4f1a\u3001\u6708\u306b\u4e00\u5ea6\u306f\u5730\u5143\u306e\u8fb2\u5bb6\u3084\u5546\u5e97\u4e3b\u3092\u8b1b\u5e2b\u306b\u62db\u304f\u300c\u307e\u3061\u306e\u5148\u751f\u300d\u8b1b\u5ea7\u304c\u958b\u304b\u308c\u308b\u3002\u5341\u4e00\u6708\u306b\u306f\u3001\u5e02\u5185\u306e\u4e2d\u5b66\u751f\u304c\u9078\u3093\u3060\u672c\u3092\u7d39\u4ecb\u3059\u308b\u5c55\u793a\u3082\u4e88\u5b9a\u3055\u308c\u3066\u3044\u308b\u3002<\/p>\n<p>\u9928\u9577\u306e\u5c71\u672c\u548c\u5b50\u3055\u3093\u306f\u300c\u672c\u3092\u501f\u308a\u308b\u5834\u6240\u304b\u3089\u3001\u4eba\u304c\u96c6\u307e\u3063\u3066\u904e\u3054\u3059\u5834\u6240\u3078\u5909\u3048\u305f\u304b\u3063\u305f\u3002\u518d\u958b\u9928\u304b\u3089\u306e\u4e00\u304b\u6708\u3067\u6765\u9928\u8005\u306f\u4ee5\u524d\u306e\u4e00\u30fb\u516b\u500d\u306b\u306a\u3063\u305f\u300d\u3068\u8a71\u3059\u3002\u5e02\u306f\u6765\u5e74\u5ea6\u3001\u6b8b\u308b\u4e8c\u3064\u306e\u56f3\u66f8\u9928\u306b\u3064\u3044\u3066\u3082\u5229\u7528\u8005\u3078\u306e\u805e\u304d\u53d6\u308a\u3092\u884c\u3044\u3001\u6539\u4fee\u306e\u5fc5\u8981\u6027\u3092\u691c\u8a0e\u3059\u308b\u65b9\u91dd\u3060\u3002<\/p>\n<\/blockquote>\n<p><em>(Summarize the following article in three sentences. (The article, about 1,200 Japanese characters, describes the renovation and reopening of a city&#8217;s central library: the budget, the new floor layout and seating, changes to the collection and e-book lending, building and accessibility work, remaining issues such as parking and staff workload, users&#8217; reactions, new events, and the city&#8217;s next steps.))<\/em><\/p>\n<pre><code class=\"language-text\">\u307f\u3069\u308a\u5e02\u306f\u7d0412\u5104\u5186\u306e\u4e8b\u696d\u8cbb\u3092\u304b\u3051\u3066\u4e2d\u592e\u56f3\u66f8\u9928\u3092\u6539\u4fee\u3057\u300110\u67081\u65e5\u306b\u518d\u958b\u9928\u3057\u305f\u3002\u6539\u4fee\u3067\u306f\u5ea7\u5e2d\u306e\u5897\u8a2d\u3068\u30d5\u30ed\u30a2\u3054\u3068\u306e\u6a5f\u80fd\u5206\u5316\u3001\u8535\u66f8\u306e\u5165\u308c\u66ff\u3048\u3001\u30d0\u30ea\u30a2\u30d5\u30ea\u30fc\u5316\u306a\u3069\u304c\u884c\u308f\u308c\u3001\u6765\u9928\u8005\u306f\u518d\u958b\u9928\u304b\u30891\u304b\u6708\u3067\u4ee5\u524d\u6bd41.8\u500d\u306b\u5897\u52a0\u3057\u305f\u3002\u4e00\u65b9\u3067\u3001\u99d0\u8eca\u5834\u4e0d\u8db3\u3084\u8077\u54e1\u8ca0\u62c5\u306e\u5897\u52a0\u3068\u3044\u3063\u305f\u8ab2\u984c\u3082\u6b8b\u3063\u3066\u304a\u308a\u3001\u5e02\u306f\u6765\u5e74\u5ea6\u3001\u6b8b\u308b2\u3064\u306e\u56f3\u66f8\u9928\u306e\u6539\u4fee\u691c\u8a0e\u3092\u9032\u3081\u308b\u65b9\u91dd\u3060\u3002\n<\/code><\/pre>\n<p><strong>Formal business email<\/strong><\/p>\n<blockquote>\n<p>\u53d6\u5f15\u5148\u306e\u62c5\u5f53\u8005\u306b\u3001\u6765\u9031\u706b\u66dc\u65e5\u306e\u6253\u3061\u5408\u308f\u305b\u3092\u6c34\u66dc\u65e5\u306b\u5909\u66f4\u3057\u3066\u307b\u3057\u3044\u3068\u304a\u9858\u3044\u3059\u308b\u30e1\u30fc\u30eb\u3092\u3001\u4e01\u5be7\u306a\u656c\u8a9e\u3067\u66f8\u3044\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/blockquote>\n<p><em>(Write an email in polite Japanese (keigo) asking a client&#8217;s contact person to move next Tuesday&#8217;s meeting to Wednesday.)<\/em><\/p>\n<pre><code class=\"language-text\">\u4ef6\u540d\uff1a\u6765\u9031\u306e\u6253\u3061\u5408\u308f\u305b\u65e5\u7a0b\u5909\u66f4\u306e\u3054\u76f8\u8ac7\n\n\u25cb\u25cb\u682a\u5f0f\u4f1a\u793e\n\u25cb\u25cb\u90e8\n\u25cb\u25cb\u69d8\n\n\u3044\u3064\u3082\u5927\u5909\u304a\u4e16\u8a71\u306b\u306a\u3063\u3066\u304a\u308a\u307e\u3059\u3002\n\u682a\u5f0f\u4f1a\u793e\u25b3\u25b3\u306e\u25b3\u25b3\u3067\u3059\u3002\n\n\u3053\u306e\u5ea6\u306f\u3001\u6765\u9031\u706b\u66dc\u65e5\uff08\u25cb\u6708\u25cb\u65e5\uff09\u306b\u4e88\u5b9a\u3057\u3066\u304a\u308a\u307e\u3059\u6253\u3061\u5408\u308f\u305b\u306e\u4ef6\u3001\n\u5927\u5909\u6050\u7e2e\u3067\u306f\u3054\u3056\u3044\u307e\u3059\u304c\u3001\u65e5\u7a0b\u306e\u5909\u66f4\u3092\u304a\u9858\u3044\u3057\u305f\u304f\u3001\u3054\u9023\u7d61\u3044\u305f\u3057\u307e\u3057\u305f\u3002\n\n\u793e\u5185\u8abf\u6574\u306e\u7d50\u679c\u3001\u706b\u66dc\u65e5\u306b\u304a\u4f3a\u3044\u3059\u308b\u3053\u3068\u304c\u96e3\u3057\u3044\u72b6\u6cc1\u3068\u306a\u308a\u307e\u3057\u305f\u305f\u3081\u3001\n\u540c\u65e5\u306e\u7fcc\u65e5\u3067\u3042\u308b\u6c34\u66dc\u65e5\uff08\u25cb\u6708\u25cb\u65e5\uff09\u306b\u5909\u66f4\u3055\u305b\u3066\u3044\u305f\u3060\u304f\u3053\u3068\u306f\u53ef\u80fd\u3067\u3057\u3087\u3046\u304b\u3002\n\n\u3054\u591a\u5fd9\u306e\u3068\u3053\u308d\u3001\u3054\u8ff7\u60d1\u3092\u304a\u304b\u3051\u3044\u305f\u3057\u307e\u3059\u3053\u3068\u3092\u6df1\u304f\u304a\u8a6b\u3073\u7533\u3057\u4e0a\u3052\u307e\u3059\u3002\n\u3054\u90fd\u5408\u304c\u3088\u308d\u3057\u3044\u3088\u3046\u3067\u3057\u305f\u3089\u3001\u6c34\u66dc\u65e5\u306e\u25cb\u6642\uff5e\u25cb\u6642\u3067\u304a\u9858\u3044\u3057\u305f\u304f\u5b58\u3058\u307e\u3059\u3002\n\n\u3054\u78ba\u8a8d\u306e\u307b\u3069\u3001\u4f55\u5352\u3088\u308d\u3057\u304f\u304a\u9858\u3044\u7533\u3057\u4e0a\u3052\u307e\u3059\u3002\n\n\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\n\u682a\u5f0f\u4f1a\u793e\u25b3\u25b3\n\u25b3\u25b3\u90e8 \u25b3\u25b3\nTEL: 03-XXXX-XXXX\nEmail: xxxxx@xxx.co.jp\n\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\n<\/code><\/pre>\n<p><strong>Word problem<\/strong><\/p>\n<blockquote>\n<p>\u308a\u3093\u3054\u304c1\u500b120\u5186\u3001\u307f\u304b\u3093\u304c1\u500b45\u5186\u3067\u3059\u3002\u308a\u3093\u3054\u30923\u500b\u3068\u307f\u304b\u3093\u30928\u500b\u8cb7\u3063\u3066\u30011,000\u5186\u672d\u3067\u6255\u3044\u307e\u3057\u305f\u3002\u304a\u3064\u308a\u306f\u3044\u304f\u3089\u3067\u3059\u304b\u3002\u8003\u3048\u65b9\u3082\u793a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/blockquote>\n<p><em>(Apples cost 120 yen each and mandarins cost 45 yen each. You buy 3 apples and 8 mandarins and pay with a 1,000-yen bill. How much change do you get? Show your reasoning.)<\/em><\/p>\n<pre><code class=\"language-text\">\u308a\u3093\u3054\u306e\u4ee3\u91d1\uff1a120\u5186 \u00d7 3\u500b = 360\u5186\n\u307f\u304b\u3093\u306e\u4ee3\u91d1\uff1a45\u5186 \u00d7 8\u500b = 360\u5186\n\u5408\u8a08\uff1a360\u5186 + 360\u5186 = 720\u5186\n\u304a\u3064\u308a\uff1a1,000\u5186 \u2212 720\u5186 = 280\u5186\n\n**\u304a\u3064\u308a\u306f280\u5186\u3067\u3059\u3002**\n<\/code><\/pre>\n<p>Reference: The correct answer is 280 yen (120\u00d73 = 360, 45\u00d78 = 360, 1,000 \u2212 720 = 280).<\/p>\n<p><strong>Knowledge<\/strong><\/p>\n<blockquote>\n<p>\u65e5\u672c\u306e\u90fd\u9053\u5e9c\u770c\u306e\u3046\u3061\u3001\u6d77\u306b\u9762\u3057\u3066\u3044\u306a\u3044\u770c\u3092\u3059\u3079\u3066\u6319\u3052\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/blockquote>\n<p><em>(List all of Japan&#8217;s prefectures that do not border the sea.)<\/em><\/p>\n<pre><code class=\"language-text\">\u65e5\u672c\u306e\u90fd\u9053\u5e9c\u770c\u3067\u6d77\u306b\u9762\u3057\u3066\u3044\u306a\u3044\u770c\u306f\u3001\u4ee5\u4e0b\u306e6\u3064\u3067\u3059\u3002\n\n1. \u5c71\u68a8\u770c\n2. \u9577\u91ce\u770c\n3. \u5c90\u961c\u770c\n4. \u611b\u77e5\u770c\n5. \u6ecb\u8cc0\u770c\n6. \u4e09\u91cd\u770c\n<\/code><\/pre>\n<p>Reference: The correct answer is eight prefectures: Tochigi, Gunma, Saitama, Yamanashi, Nagano, Gifu, Shiga and Nara.<\/p>\n<p><strong>Constrained writing<\/strong><\/p>\n<blockquote>\n<p>\u300c\u6625\u300d\u3092\u30c6\u30fc\u30de\u306b\u3057\u305f\u4ff3\u53e5\u30923\u3064\u3001\u4e94\u30fb\u4e03\u30fb\u4e94\u306e\u97f3\u6570\u3092\u5b88\u3063\u3066\u4f5c\u3063\u3066\u304f\u3060\u3055\u3044\u3002\u305d\u308c\u305e\u308c\u306b\u4e00\u8a00\u306e\u8aac\u660e\u3092\u4ed8\u3051\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/blockquote>\n<p><em>(Write three haiku on the theme of spring, keeping the 5-7-5 syllable pattern, each with a one-line explanation.)<\/em><\/p>\n<pre><code class=\"language-text\">\u3054\u4f9d\u983c\u306e\u901a\u308a\u3001\u300c\u6625\u300d\u3092\u30c6\u30fc\u30de\u306b\u3057\u305f\u4e94\u30fb\u4e03\u30fb\u4e94\u306e\u97f3\u6570\u3092\u53b3\u5bc6\u306b\u5b88\u3063\u305f\u4ff3\u53e5\u30923\u3064\u4f5c\u6210\u3057\u307e\u3057\u305f\u3002\n\n**1. \u53e5**\n&gt; **\u6625\u306e\u98a8 \u685c\u5439\u96ea\u306e \u821e\u3044\u843d\u3061\u308b**\n\n*   **\u97f3\u6570\u78ba\u8a8d**: \u6625(1)\u306e(1)\u98a8(1) \/ \u685c(1)\u5439(1)\u96ea(1)\u306e(1) \/ \u821e(1)\u3044(1)\u843d(1)\u3061(1)\u308b(1) \u2192 **5\u30fb7\u30fb5**\n*   **\u8aac\u660e**: \u6625\u306e\u98a8\u306b\u4e57\u3063\u3066\u821e\u3044\u6563\u308b\u685c\u306e\u82b1\u3073\u3089\uff08\u5439\u96ea\uff09\u306e\u3001\u4e00\u77ac\u306e\u7f8e\u3057\u3055\u3068\u511a\u3055\u3092\u63cf\u3044\u305f\u53e5\u3067\u3059\u3002\n\n**2. \u53e5**\n&gt; **\u83dc\u306e\u82b1 \u54b2\u304d\u4e71\u308c\u3066 \u6625\u306e\u91ce**\n\n*   **\u97f3\u6570\u78ba\u8a8d**: \u83dc(1)\u306e(1)\u82b1(1) \/ \u54b2(1)\u304d(1)\u4e71(1)\u308c(1)\u3066(1) \/ \u6625(1)\u306e(1)\u91ce(1) \u2192 **5\u30fb7\u30fb5**\n*   **\u8aac\u660e**: \u6625\u306e\u91ce\u539f\u4e00\u9762\u306b\u54b2\u304d\u8a87\u308b\u83dc\u306e\u82b1\u306e\u3001\u529b\u5f37\u304f\u660e\u308b\u3044\u751f\u547d\u529b\u3092\u8868\u73fe\u3057\u305f\u53e5\u3067\u3059\u3002\n\n**3. \u53e5**\n&gt; **\u9daf\u306e \u58f0\u306b\u8a98\u308f\u308c \u6625\u306e\u671d**\n\n*   **\u97f3\u6570\u78ba\u8a8d**: \u9daf(1)\u306e(1) \/ \u58f0(1)\u306b(1)\u8a98(1)\u308f(1)\u308c(1) \/ \u6625(1)\u306e(1)\u671d(1) \u2192 **5\u30fb7\u30fb5**\n*   **\u8aac\u660e**: \u6625\u306e\u671d\u3001\u9daf\u306e\u7f8e\u3057\u3044\u9cf4\u304d\u58f0\u306b\u8033\u3092\u50be\u3051\u3001\u6625\u306e\u8a2a\u308c\u3092\u5b9f\u611f\u3059\u308b\u9759\u304b\u306a\u60c5\u666f\u3092\u63cf\u3044\u305f\u53e5\u3067\u3059\u3002\n<\/code><\/pre>\n<p><!-- \/lmw:lab --><\/p>\n<h2>How to Get It<\/h2>\n<p>It is distributed in GGUF format and can be downloaded from Hugging Face.<\/p>\n<p>In a llama.cpp environment, you can install and start the server with the following commands:<\/p>\n<pre><code class=\"language-bash\">curl -LsSf https:\/\/llama.app\/install.sh | sh\nllama-server -hf bartowski\/vectionlabs_Salience-27B-R6-GGUF:Q4_K_M\n<\/code><\/pre>\n<p>Example command for individual download using the Hugging Face CLI:<\/p>\n<pre><code class=\"language-bash\">hf download bartowski\/vectionlabs_Salience-27B-R6-GGUF --include &quot;vectionlabs_Salience-27B-R6-Q4_K_M.gguf&quot; --local-dir.\/\n<\/code><\/pre>\n<p>In addition to llama.cpp, this model is compatible with LM Studio, koboldcpp, ramalama, Jan AI, Text Generation Web UI, LoLLMs, Atomic Chat, and others.<\/p>\n<p>When using image recognition, you need to specify (<code>--mmproj<\/code>) the bundled multimodal projector file (<code>mmproj-vectionlabs_Salience-27B-R6-f16.gguf<\/code> or <code>bf16<\/code>). Also, to use acceleration via MTP, append <code>--spec-type draft-mtp<\/code> to the command line.<\/p>\n<p><!-- lmw:related --><\/p>\n<h2>Related Articles<\/h2>\n<ul>\n<li><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/10\/02\/cloudflare-clef-27b-multimodal-decision-model\/\">clef Structured Decision-Making Model: 12GB+ VRAM, GGUF Builds<\/a><\/li>\n<li><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/28\/hemmingway-1-open-27b-model-specialized-for-human-like-writing\/\">Hemmingway-1 Text Generation Model: 12GB+ VRAM, GGUF Builds<\/a><\/li>\n<li><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/28\/orcasaq2-27b-qwen3-8-27b-quantized\/\">OrcaSAQ-2-27B Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a><\/li>\n<li><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/26\/qwen3-8-27b-overview-specs-performance\/\">Qwen3.8-27B Vision-Language Model: Our Test Answers, 8GB+ VRAM<\/a><\/li>\n<\/ul>\n<p><!-- \/lmw:related --><\/p>\n<p><!-- lmw:next-steps --><\/p>\n<h2>What to Read Next<\/h2>\n<ul>\n<li><strong>Find models by VRAM<\/strong> (This model runs from the 12GB tier) \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/vram-guide-12gb-en\/\">Other models that run on a 12GB GPU<\/a><\/li>\n<li><strong>Explore the same model family<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/model-qwen-qwen3-8-en\/\">Qwen3.8 family overview (8 articles, 16 converted builds)<\/a><\/li>\n<li><strong>Engines that run this model<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/engine-llama-cpp-en\/\">llama.cpp<\/a> \/ <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/engine-ollama-en\/\">Ollama<\/a><\/li>\n<li><strong>What IQ2_M, Q3_K_L, Q5_K_M mean and where to get this model<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/format-gguf-en\/\">GGUF format guide and models<\/a> \/ <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/glossary-quantization-en\/\">Quantization and model-format glossary<\/a><\/li>\n<li><strong>Learn about the publisher<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/publisher-alibaba-en\/\">Alibaba (Qwen): models, licenses and articles<\/a><\/li>\n<\/ul>\n<p><!-- \/lmw:next-steps --><\/p>\n<h2>Sources<\/h2>\n<ul>\n<li><a href=\"https:\/\/huggingface.co\/bartowski\/vectionlabs_Salience-27B-R6-GGUF\">https:\/\/huggingface.co\/bartowski\/vectionlabs_Salience-27B-R6-GGUF<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/vectionlabs\/Salience-27B-R6\">https:\/\/huggingface.co\/vectionlabs\/Salience-27B-R6<\/a><\/li>\n<\/ul>\n<p><!-- lmw:updates --><\/p>\n<h2>Update History<\/h2>\n<ul>\n<li>2026-09-24: Rebuilt the article: Added a &#8220;can you run it locally?&#8221; verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n<li>2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n<li>2026-09-28: Added our own measurements: Japanese token efficiency, what is inside the GGUF file.<\/li>\n<li>2026-09-29: Added our own measurements: CPU speed and answers to Japanese questions.<\/li>\n<li>2026-09-30: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n<\/ul>\n<p><!-- \/lmw:updates --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>bartowski has released GGUF quantizations for Vection Labs&#8217; Salience-27B-R6, a multimodal 27B Dense model with reasoning economy.<\/p>\n","protected":false},"author":1,"featured_media":7663,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[1030,163,136,1257,1259,896,117],"class_list":["post-672","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-bartowski-en","tag-gguf-en","tag-llm-en","tag-salience-27b-r6-en","tag-vection-labs-en","tag--en"],"lang":"en","translations":{"en":672,"ja":670},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/672","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=672"}],"version-history":[{"count":41,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/672\/revisions"}],"predecessor-version":[{"id":14257,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/672\/revisions\/14257"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/7663"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=672"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=672"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=672"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}