{ "data": { "a": { "slug": "lfm3-vl-2b", "name": "LFM3-VL-2B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM3-VL-2B is Liquid AI's open-weight 1.92B vision-language model for on-device use, taking text, images and video with thinking, tool calling and a 128K-token context, released on 27 October 2026 with Liquid's own GGUF, FP8, NVFP4, MLX and ONNX builds.", "url": "https://www.anchorterminal.com/tools/lfm3-vl-2b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json", "dns": "lfm3-vl-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/LiquidAI/LFM3-VL-2B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM3-VL-2B" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-VL-2B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-VL-2B-MLX-4bit" }, { "registry": "ollama", "name": "lfm3-vl" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.10 GB for Liquid's Q4_K_M GGUF with its 0.20 GB projector file and 3.84 GB in BF16. Liquid's API also serves it, at prices shown in its console (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://docs.liquid.ai/lfm/models/lfm3-vl-2b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "vision", "video", "small", "edge", "reasoning", "tool-calling", "custom-licence", "safetensors", "gguf", "mlx", "onnx", "self-hosted" ], "lastRelease": "2026-11-09", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 58, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 90, "reach": 85, "risk": 40, "upkeep": 77 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.", "bestFor": "Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits", "strengths": [ "Reads images and video on an 8 GB machine from Liquid's own GGUF", "Tool calling, deferred tools and thinking in a 1.92B vision model", "A 128K-token context", "Liquid answered most questions on the repository", "Liquid's own API serves it" ], "weaknesses": [ "vLLM and SGLang support is unreleased, which the card says itself", "Loading in Transformers runs code from the repository", "No safety results and no security contact", "How to ask for JSON isn't documented for this model", "The licence stops free commercial use at $10M in annual revenue", "Not recommended for coding, multi-turn conversations or complex reasoning" ], "agentNotes": [ "Start it with `llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-vl`, with llama.cpp b11960 or later", "Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card", "Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg", "Don't plan on vLLM or SGLang until a release carries LFM3-VL", "Validate JSON output yourself", "Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 90, "reach": 85, "risk": 40, "upkeep": 77 }, "provenanceScore": 73, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-vl", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lfm3-vl-2b" }, "sameCompany": [ "liquid-d1", "lfm2-5-2-6b", "lfm3-1-7b", "lfm3-audio-2b", "lfm3-omni-2b" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "LiquidAI/LFM3-1.7B-Base", "params": "1.92B", "context": 128000, "goodFor": "Small on-device vision model for questions about video, documents, charts and app screens, finding objects and spatial reasoning, not for coding, multi-turn conversations or complex reasoning.", "runtimes": [ "llama.cpp", "Ollama", "LM Studio" ], "builds": [ { "name": "LFM3-VL-2B", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B", "who": "publisher", "precision": "bf16", "sizeGB": 3.84, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ], "note": "Loads in Transformers with the repository's own code. Liquid also publishes FP8 (2.59 GB) and NVFP4 (2.21 GB) safetensors builds and an ONNX repository" }, { "name": "LFM3-VL-2B-GGUF Q8_0", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 2.05, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "LFM3-VL-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-VL-2B-Q8_0.gguf (0.20 GB)" }, { "name": "LFM3-VL-2B-GGUF Q4_K_M", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.3, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "LFM3-VL-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-VL-2B-Q8_0.gguf (0.20 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files" }, { "name": "LFM3-VL-2B-MLX-4bit", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 1.59, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX-VLM" ], "note": "Liquid also publishes 5, 6 and 8-bit and BF16 MLX builds" }, { "name": "ollama lfm3-vl", "url": "https://ollama.com/library/lfm3-vl", "who": "ollama", "precision": "q4_k_m", "sizeGB": 1.5, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Ollama's library build, size as its tags page shows it on 10 November 2026. The library also has q8_0, bf16, mxfp8 and nvfp4 tags" } ], "run": "llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja", "runBuild": "LFM3-VL-2B-GGUF Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Liquid AI", "family": "LFM3", "url": "https://www.anchorterminal.com/open-weights/#liquid", "others": [ { "name": "LFM3-1.7B-Base", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-Base", "params": "1.73B" }, { "name": "LFM3-1.7B", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B", "params": "1.73B", "listing": "https://www.anchorterminal.com/tools/lfm3-1-7b" }, { "name": "LFM3-Omni-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "params": "2.17B", "listing": "https://www.anchorterminal.com/tools/lfm3-omni-2b" }, { "name": "LFM3-Audio-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B", "params": "2.00B", "listing": "https://www.anchorterminal.com/tools/lfm3-audio-2b" } ] }, "area": "models", "provenance": { "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)", "domain": "liquid.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/LiquidAI/LFM3-VL-2B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The licence names Liquid AI, Inc. as licensor. The domain's registration date is as recorded on the liquid-d1 listing on 8 October 2026", "www.liquid.ai/.well-known/security.txt answered 404 on 10 November 2026" ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.json" }, "answer": "Muse Glimmer 30B scores 70 (BB) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-VL-2B leads on agent interface and documentation and upkeep.", "b": { "slug": "muse-glimmer-30b", "name": "Muse Glimmer 30B", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai/docs/muse-glimmer", "kind": "weights", "category": "open-weights", "summary": "Muse Glimmer 30B is Meta's open-weight dense multimodal model for local agent work, 29.6B parameters with text and image input and a 128K-token context, published on Hugging Face under Apache 2.0 with GGUF and ExecuTorch builds.", "url": "https://www.anchorterminal.com/tools/muse-glimmer-30b", "markdownUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json", "dns": "muse-glimmer-30b.grade.anchorterminal.com", "repo": "https://huggingface.co/meta-models/Muse-Glimmer-30B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "meta-models/Muse-Glimmer-30B" }, { "registry": "huggingface", "name": "meta-models/Muse-Glimmer-30B-GGUF" }, { "registry": "huggingface", "name": "meta-models/Muse-Glimmer-30B-assistant" }, { "registry": "huggingface", "name": "meta-models/Muse-Glimmer-30B-ExecuTorch-PTE" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-10-21), and Meta's docs say there is no API key and no gated access.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with a separate Muse Glimmer Usage Policy listing prohibited uses. Meta Model API doesn't serve it. Running it yourself costs the hardware, 17 GB for Meta's Q4_K_M GGUF or about 60 GB in BF16 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://dev.meta.ai/docs/muse-glimmer", "llmsTxt": "https://dev.meta.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "self-hosted", "new" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 70, "grade": "BB", "agentReady": true, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 5, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 50, "interface": 75, "reach": 90, "risk": 80, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "negative": 0, "verdict": "A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0.", "bestFor": "Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough", "strengths": [ "Meta's own 16.8 GB Q4_K_M GGUF fits a 24 GB card, with a DFlash drafter for speculative decoding", "Released model code in vLLM, SGLang, Transformers, llama.cpp and Ollama, no trust_remote_code", "Tool format and parser names per runtime documented by Meta, with a Jinja template in the repository", "Trained against indirect prompt injection with a principal hierarchy, and injection results published", "Ungated download, safetensors and GGUF from Meta's verified organisation, and three live inference providers" ], "weaknesses": [ "One tool call per turn and no parallel calls", "No structured output documented, and reasoning can't be turned off", "A usage policy adds prohibited uses beyond the law to Apache 2.0", "Meta replied to 1 of the 40 most recent discussions, and a chat template bug report has waited since 11 August", "Safety results are Meta's own, with nothing published to rerun them" ], "agentNotes": [ "Start it with `llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'` on llama.cpp b10353 or later, or `ollama run muse-glimmer` on Ollama 0.32.7 or later", "In vLLM pass `--tool-call-parser muse_glimmer --reasoning-parser muse_glimmer`, in SGLang `muse` for both, in ExecuTorch `--tool-parser atem`. Without them tool calls come back as plain text", "Set reasoning per request with `chat_template_kwargs.reasoning_strength` (low, medium, high or xhigh). llama.cpp and ExecuTorch don't read `reasoning_effort`, and ExecuTorch rejects it with a 400", "Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects `tool_choice=\"required\"`", "Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on `\u003c|eom|\u003e`", "Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length", "For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 70 } ], "editorialScores": { "capability": 50, "interface": 75, "reach": 90, "risk": 80, "upkeep": 65 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-11-01", "interface": "2026-11-01", "reach": "2026-11-01", "risk": "2026-11-01", "upkeep": "2026-11-01" }, "last": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run muse-glimmer\n# or Meta's 17 GB GGUF with llama.cpp b10353 or later:\nllama serve -hf meta-models/Muse-Glimmer-30B-GGUF \\\n --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf \\\n -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 \\\n --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"muse-glimmer\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/muse-glimmer-30b" }, "sameCompany": [ "meta-model-api", "muse-ember", "muse-spark-open", "muse-code" ], "weights": { "trainedBy": "Meta", "builtOn": "Distilled from Muse Spark", "params": "29.6B", "context": 131072, "goodFor": "Dense multimodal model for local agent work, text and images in, with tool calls and always-on adjustable reasoning.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Muse-Glimmer-30B-KQuant-Dynamic-Q4_K_XL", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/blob/main/Muse-Glimmer-30B-KQuant-Dynamic-Q4_K_XL.gguf", "who": "publisher", "precision": "q4_k_xl", "sizeGB": 19.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Meta's dynamic K-quant; the card targets 32 GB. Add mmproj-Muse-Glimmer-30B-Q4_K_M.gguf (1.4 GB) for images" }, { "name": "Muse-Glimmer-30B-KQuant-17GB-Q4_K_M", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/blob/main/Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 16.8, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "The card targets 24 GB. Add the 1.4 GB mmproj for images and the 1.6 GB DFlash drafter for speculative decoding" }, { "name": "Muse-Glimmer-30B-ExecuTorch-PTE (smallest: k-quant-17G text-solo, Metal)", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-ExecuTorch-PTE", "who": "publisher", "precision": "q4_k_m", "sizeGB": 17.9, "memoryGB": 24, "format": "executorch", "note": "16 ExecuTorch exports from 17.9 to 31.4 GB, for Apple Metal or NVIDIA (sm80) only" }, { "name": "ollama muse-glimmer:latest", "url": "https://ollama.com/library/muse-glimmer:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 18, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a 1.4 GB vision projector. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "Muse-Glimmer-30B", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B", "who": "publisher", "precision": "bf16", "sizeGB": 59.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'", "runBuild": "Muse-Glimmer-30B-KQuant-17GB-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Meta", "family": "Muse", "url": "https://www.anchorterminal.com/open-weights/#meta", "others": [ { "name": "Muse Glimmer 30B assistant (DFlash drafter)", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-assistant", "params": "2.56B (Hub safetensors count; the card states no figure)" }, { "name": "Muse Spark Open 816B-A42B", "url": "https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B", "params": "816B total, 42B active (MoE)", "listing": "https://www.anchorterminal.com/tools/muse-spark-open" } ], "olderCount": 50, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Meta (the verified Hugging Face organisation is \"Meta Inc.\", the card names Meta Superintelligence Lab, and the licence carries no copyright line)", "domain": "meta.ai", "domainRegistered": "2017-12-16", "domainNote": "Registration date as recorded by RDAP for the muse-spark-open listing.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/meta-models/Muse-Glimmer-30B/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "meta-models is a verified Hugging Face organisation named Meta Inc. Meta's launch post on research.meta.ai and its developer docs on dev.meta.ai link to huggingface.co/meta-models/Muse-Glimmer-30B, and the same organisation holds Muse Spark Open.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Some messages say what changed (the duplicate reasoning-strength fix on 11 August); 7 of the 15 read Update README.md.", "dev.meta.ai/.well-known/security.txt and research.meta.ai/.well-known/security.txt answered 404 on 21 October 2026, and ai.meta.com's redirected and wasn't followed. The usage policy sends bugs and security concerns to facebook.com/whitehat/info." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.json", "live": { "slug": "muse-glimmer-30b", "securityTxt": { "url": "https://meta.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:58.767810687Z" }, "llmsTxt": { "url": "https://dev.meta.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-10T15:44:02.548249718Z" }, "domain": { "domain": "meta.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/meta.ai", "checkedAt": "2026-11-08T14:51:01.706547979Z" }, "pages": [ { "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:04.598495005Z", "changedAt": "2026-11-10T16:23:04.598495005Z", "fingerprint": "fe9fc096c77c" } ], "updatedAt": "2026-11-10T16:23:04.598495005Z" } }, "edges": [ { "by": 40, "category": "risk", "leads": "muse-glimmer-30b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.", "b": "File safety 25, safetensors and GGUF, with the ExecuTorch pos_embed.bin files read as raw embedding tables by their exact size and the Hub's scans, and no repository code runs. Provenance 15, the verified meta-models organisation Meta links, with a checksum on every file. Safety evaluation 10, Meta's own published results with stated set-ups but nothing to rerun and no outside assessor. Agent guidance 15, trained against indirect prompt injection with a principal hierarchy, and results published. Licence risk 10, 5 off for the use policy, and the policy's pointer to a latest copy leads back to the same repository file with no change clause. Security programme 5, a whitehat route in the usage policy, with no security.txt and public advisories unchecked, so scored as absent." }, "scores": { "a": 40, "b": 80 } }, { "by": 15, "category": "interface", "leads": "lfm3-vl-2b", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says its fine-tuning mix included tool calling and agentic traces. Structured output 5, the card's vLLM and SGLang commands set a structured-output backend and its fine-tuning table has a JSON extraction notebook, but nothing says how to ask this model for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, Liquid's chat-template page documents `enable_thinking=False` in apply_chat_template for LFM3, with reasoning on by default inside think tags, and the card documents `preserve_thinking` and reports a no-thinking configuration in its benchmarks. Context 10, 128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. 20 + 25 + 20 + 5 + 10 + 10 = 90.", "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Meta documents declaring tools, the ATEM block that comes back and the parser for each runtime. Trained tool calling 10, one call per turn and no parallel calls. Structured output 0, nothing documented for JSON or JSON Schema. Reasoning control 10, reasoning can't be turned off but is budgeted per request with four strength levels and returned as reasoning_content. Context 10, 131,072 tokens trained in." }, "scores": { "a": 90, "b": 75 } }, { "by": 12, "category": "upkeep", "leads": "lfm3-vl-2b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77.", "b": "Model card 20, all seven parts present, languages as a count of more than 100. Training data 10, a general description with a 4 January 2026 cut-off and no sources or sizes. Last change 20, a card update on 20 October citing the technical report, read as a change to the files as card edits were for other listings; Meta added its own MMI evaluation results file on 22 October. Responsiveness 5, a Meta member replied to 1 of the 40 most recent non-PR discussions (re-counted on 1 November). Version policy 5, a first release. File health 5, the files agree but a chat template bug report from 11 August still has no reply." }, "scores": { "a": 77, "b": 65 } }, { "by": 5, "category": "reach", "leads": "muse-glimmer-30b", "name": "Reach", "reasons": { "a": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.", "b": "Memory 25, Meta's 16.8 GB Q4_K_M GGUF needs about 20 GB with the runtime's fifth, so it fits a 24 GB card but not 16 GB. Runtimes 25, released model code in vLLM, SGLang, Transformers, llama.cpp and Ollama's library fills the cap. Builds 15, Meta publishes GGUF and ExecuTorch 4-bit builds, read as two kinds as gemma-4-31b-it was, though all are 4-bit k-quants. Terms 10, Apache 2.0 with a usage policy that adds limits beyond the law, so a free commercial licence under a use policy. Download 10, no repository is gated. Hosted 5, Meta's API doesn't serve it but Together, Featherless AI and DeepInfra are live on its Hub page." }, "scores": { "a": 85, "b": 90 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Liquid AI", "b": "Meta", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-09", "b": "2026-10-20", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "3.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Muse Glimmer 30B scores 70 (BB) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-VL-2B leads on agent interface and documentation and upkeep.", "question": "Which is better for AI agents, LFM3-VL-2B or Muse Glimmer 30B?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 90 against 75", "Documentation and upkeep, 77 against 65" ], "also": null, "goodFor": "Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits", "slug": "lfm3-vl-2b", "watchFor": "vLLM and SGLang support is unreleased, which the card says itself" }, { "aheadOn": [ "Reach, 90 against 85", "Risk, 80 against 40" ], "also": [ "Agent-ready, a grade of BB or better" ], "goodFor": "Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough", "slug": "muse-glimmer-30b", "watchFor": "One tool call per turn and no parallel calls" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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{ "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b.json", "title": "Muse Glimmer 30B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-397b.json", "title": "Muse Glimmer 30B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b.json", "title": "Muse Glimmer 30B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-otel-2-0-llm-31b-it.json", "title": "Muse Glimmer 30B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-otel-2-0-llm-31b-it" }, { "json": 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"https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-122b-a10b.json", "title": "Muse Glimmer 30B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-2b.json", "title": "Muse Glimmer 30B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-397b-a17b.json", "title": "Muse Glimmer 30B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-4b.json", "title": "Muse Glimmer 30B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b.json", "title": "Muse Glimmer 30B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-27b.json", "title": "Muse Glimmer 30B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b.json", "title": "Muse Glimmer 30B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-27b.json", "title": "Muse Glimmer 30B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next.json", "title": "Muse Glimmer 30B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-next.json", "title": "Muse Glimmer 30B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-2b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-32b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct.json", "title": "Muse Glimmer 30B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2.json", "title": "Muse Glimmer 30B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-recursive-flint.json", "title": "Muse Glimmer 30B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ripple-65b-preview.json", "title": "Muse Glimmer 30B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-sensenova-loop-9b-preview.json", "title": "Muse Glimmer 30B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b.json", "title": "Muse Glimmer 30B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b.json", "title": "Muse Glimmer 30B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-step-3-7-flash.json", "title": "Muse Glimmer 30B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b.json", "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-thinkingcap-qwen3-8-27b.json", "title": "Muse Glimmer 30B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-unlimited-ocr-2.json", "title": "Muse Glimmer 30B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b.json", "title": "Muse Glimmer 30B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b.json", "title": "Muse Glimmer 30B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-zaya1-8b.json", "title": "Muse Glimmer 30B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "muse-glimmer-30b", "key": "reach", "lfm3-vl-2b": 85, "muse-glimmer-30b": 90, "name": "Reach", "weight": 25 }, { "by": 40, "edge": "muse-glimmer-30b", "key": "risk", "lfm3-vl-2b": 40, "muse-glimmer-30b": 80, "name": "Risk", "weight": 20 }, { "by": 15, "edge": "lfm3-vl-2b", "key": "interface", "lfm3-vl-2b": 90, "muse-glimmer-30b": 75, "name": "Agent interface", "weight": 10 }, { "by": 12, "edge": "lfm3-vl-2b", "key": "upkeep", "lfm3-vl-2b": 77, "muse-glimmer-30b": 65, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Muse Glimmer 30B scores 70 (BB) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-VL-2B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "lfm3-vl-2b": "A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.", "muse-glimmer-30b": "A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-muse-glimmer-30b", "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-muse-glimmer-30b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-muse-glimmer-30b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-muse-glimmer-30b.min.md" }, "markdown": "Muse Glimmer 30B scores 70 (BB) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-VL-2B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- LFM3-VL-2B: grade C, 61/100, rank #72 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json\n- Muse Glimmer 30B: grade BB, 70/100, rank #7 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/muse-glimmer-30b.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LFM3-VL-2B (C)\n\nGood for: Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits\n\nAhead on:\n- Agent interface, 90 against 75\n- Documentation and upkeep, 77 against 65\n\nWatch for: vLLM and SGLang support is unreleased, which the card says itself\n\n### Muse Glimmer 30B (BB)\n\nGood for: Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough\n\nAhead on:\n- Reach, 90 against 85\n- Risk, 80 against 40\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: One tool call per turn and no parallel calls\n\n\n## Why the edge\n\n### Risk: Muse Glimmer 30B +40\n\n- LFM3-VL-2B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.\n- Muse Glimmer 30B (80): File safety 25, safetensors and GGUF, with the ExecuTorch pos_embed.bin files read as raw embedding tables by their exact size and the Hub's scans, and no repository code runs. Provenance 15, the verified meta-models organisation Meta links, with a checksum on every file. Safety evaluation 10, Meta's own published results with stated set-ups but nothing to rerun and no outside assessor. Agent guidance 15, trained against indirect prompt injection with a principal hierarchy, and results published. Licence risk 10, 5 off for the use policy, and the policy's pointer to a latest copy leads back to the same repository file with no change clause. Security programme 5, a whitehat route in the usage policy, with no security.txt and public advisories unchecked, so scored as absent.\n\n### Agent interface: LFM3-VL-2B +15\n\n- LFM3-VL-2B (90): Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says its fine-tuning mix included tool calling and agentic traces. Structured output 5, the card's vLLM and SGLang commands set a structured-output backend and its fine-tuning table has a JSON extraction notebook, but nothing says how to ask this model for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, Liquid's chat-template page documents `enable_thinking=False` in apply_chat_template for LFM3, with reasoning on by default inside think tags, and the card documents `preserve_thinking` and reports a no-thinking configuration in its benchmarks. Context 10, 128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. 20 + 25 + 20 + 5 + 10 + 10 = 90.\n- Muse Glimmer 30B (75): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Meta documents declaring tools, the ATEM block that comes back and the parser for each runtime. Trained tool calling 10, one call per turn and no parallel calls. Structured output 0, nothing documented for JSON or JSON Schema. Reasoning control 10, reasoning can't be turned off but is budgeted per request with four strength levels and returned as reasoning_content. Context 10, 131,072 tokens trained in.\n\n### Documentation and upkeep: LFM3-VL-2B +12\n\n- LFM3-VL-2B (77): Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77.\n- Muse Glimmer 30B (65): Model card 20, all seven parts present, languages as a count of more than 100. Training data 10, a general description with a 4 January 2026 cut-off and no sources or sizes. Last change 20, a card update on 20 October citing the technical report, read as a change to the files as card edits were for other listings; Meta added its own MMI evaluation results file on 22 October. Responsiveness 5, a Meta member replied to 1 of the 40 most recent non-PR discussions (re-counted on 1 November). Version policy 5, a first release. File health 5, the files agree but a chat template bug report from 11 August still has no reply.\n\n### Reach: Muse Glimmer 30B +5\n\n- LFM3-VL-2B (85): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.\n- Muse Glimmer 30B (90): Memory 25, Meta's 16.8 GB Q4_K_M GGUF needs about 20 GB with the runtime's fifth, so it fits a 24 GB card but not 16 GB. Runtimes 25, released model code in vLLM, SGLang, Transformers, llama.cpp and Ollama's library fills the cap. Builds 15, Meta publishes GGUF and ExecuTorch 4-bit builds, read as two kinds as gemma-4-31b-it was, though all are 4-bit k-quants. Terms 10, Apache 2.0 with a usage policy that adds limits beyond the law, so a free commercial licence under a use policy. Download 10, no repository is gated. Hosted 5, Meta's API doesn't serve it but Together, Featherless AI and DeepInfra are live on its Hub page.\n\n## Score by category\n\n| Category | Weight | LFM3-VL-2B | Muse Glimmer 30B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 90 | Muse Glimmer 30B +5 |\n| Risk | 20% (20 this run) | 40 | 80 | Muse Glimmer 30B +40 |\n| Agent interface | 10% (10 this run) | 90 | 75 | LFM3-VL-2B +15 |\n| Documentation and upkeep | 10% (10 this run) | 77 | 65 | LFM3-VL-2B +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **70 · BB** | |\n\n## Facts side by side\n\n| Fact | LFM3-VL-2B | Muse Glimmer 30B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | Meta |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-09 | 2026-10-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3.8/5 (4) |\n\n## Verdicts\n\n**LFM3-VL-2B.** A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.\n\n**Muse Glimmer 30B.** A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0.\n\n## Before you call either\n\n### LFM3-VL-2B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-vl`, with llama.cpp b11960 or later\n2. Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card\n3. Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg\n4. Don't plan on vLLM or SGLang until a release carries LFM3-VL\n5. Validate JSON output yourself\n6. Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning\n\n### Muse Glimmer 30B\n\n1. Start it with `llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'` on llama.cpp b10353 or later, or `ollama run muse-glimmer` on Ollama 0.32.7 or later\n2. In vLLM pass `--tool-call-parser muse_glimmer --reasoning-parser muse_glimmer`, in SGLang `muse` for both, in ExecuTorch `--tool-parser atem`. Without them tool calls come back as plain text\n3. Set reasoning per request with `chat_template_kwargs.reasoning_strength` (low, medium, high or xhigh). llama.cpp and ExecuTorch don't read `reasoning_effort`, and ExecuTorch rejects it with a 400\n4. Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects `tool_choice=\"required\"`\n5. Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on `\u003c|eom|\u003e`\n6. Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length\n7. For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth\n\n## Questions\n\n### Which is better for AI agents, LFM3-VL-2B or Muse Glimmer 30B?\n\nMuse Glimmer 30B scores 70 (BB) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-VL-2B leads on agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-muse-glimmer-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-muse-glimmer-30b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-vl-2b\", \"b\": \"muse-glimmer-30b\"}`. 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