{ "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-10", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "tool": { "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": 56, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 90, "reach": 85, "risk": 40, "upkeep": 77 }, "pending": [ "performance", "tasks" ], "breakdown": [ { "key": "capability", "name": "Capability", "weight": 35, "effectiveWeight": 0, "pending": true, "points": 0, "reason": "Pending for this listing. Nobody independent has published results for it in at least two areas yet, so its weight is shared across the other four and the total is capped at 61 until they do or our own runs start." }, { "key": "reach", "name": "Reach", "weight": 25, "effectiveWeight": 38.46, "score": 85, "points": 32.69, "reason": "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.", "checked": "2026-11-10" }, { "key": "risk", "name": "Risk", "weight": 20, "effectiveWeight": 30.77, "score": 40, "points": 12.31, "reason": "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.", "checked": "2026-11-10" }, { "key": "interface", "name": "Agent interface", "weight": 10, "effectiveWeight": 15.38, "score": 90, "points": 13.85, "reason": "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.", "checked": "2026-11-10" }, { "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "effectiveWeight": 15.38, "score": 77, "points": 11.85, "reason": "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.", "checked": "2026-11-10" } ], "assessment": { "date": "2026-11-10", "basis": "public evidence", "confidence": "low", "notes": { "capability": "Pending for this listing. Independent results were found in 0 areas (none), and Capability needs 2. Its weight is shared across the other four categories and the total is capped at 61 until there are more, or our own runs start.", "interface": "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.", "reach": "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.", "risk": "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.", "upkeep": "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." }, "sources": [ { "what": "model card", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B", "seen": "2026-11-10" }, { "what": "gating, dates, parameter count, downloads, likes, file list and hashes", "url": "https://huggingface.co/api/models/LiquidAI/LFM3-VL-2B?blobs=true", "seen": "2026-11-10" }, { "what": "inference providers, none mapped", "url": "https://huggingface.co/api/models/LiquidAI/LFM3-VL-2B?expand[]=inferenceProviderMapping", "seen": "2026-11-10" }, { "what": "commit history", "url": "https://huggingface.co/api/models/LiquidAI/LFM3-VL-2B/commits/main", "seen": "2026-11-10" }, { "what": "architecture, auto_map and context length", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B/raw/main/config.json", "seen": "2026-11-10" }, { "what": "chat template", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B/raw/main/chat_template.jinja", "seen": "2026-11-10" }, { "what": "licence, head read", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B/raw/main/LICENSE", "seen": "2026-11-10" }, { "what": "discussions, replies counted by script from authors, dates and member flags", "url": "https://huggingface.co/api/models/LiquidAI/LFM3-VL-2B/discussions", "seen": "2026-11-10" }, { "what": "Liquid's LFM3 repositories, their dates and downloads", "url": "https://huggingface.co/api/models?author=LiquidAI\u0026search=LFM3", "seen": "2026-11-10" }, { "what": "Liquid's GGUF files and sizes", "url": "https://huggingface.co/api/models/LiquidAI/LFM3-VL-2B-GGUF?blobs=true", "seen": "2026-11-10" }, { "what": "Liquid's 4-bit MLX build", "url": "https://huggingface.co/api/models/LiquidAI/LFM3-VL-2B-MLX-4bit?blobs=true", "seen": "2026-11-10" }, { "what": "Ollama's library tags", "url": "https://ollama.com/library/lfm3-vl/tags", "seen": "2026-11-10" }, { "what": "LM Studio's catalogue, lfm3-vl-2b listed and no page for the others", "url": "https://lmstudio.ai/models/liquid/lfm3-vl-2b", "seen": "2026-11-10" }, { "what": "llama.cpp b12242, the lfm3 architecture (shallow clone)", "url": "https://raw.githubusercontent.com/ggml-org/llama.cpp/b12242/src/models/lfm3.cpp", "seen": "2026-11-10" }, { "what": "llama.cpp b12242, LFM3 conversion with the vision and audio projectors", "url": "https://raw.githubusercontent.com/ggml-org/llama.cpp/b12242/conversion/lfm3.py", "seen": "2026-11-10" }, { "what": "vLLM v0.33.0 registry, no lfm3", "url": "https://raw.githubusercontent.com/vllm-project/vllm/v0.33.0/vllm/model_executor/models/registry.py", "seen": "2026-11-10" }, { "what": "SGLang v0.5.23 and Transformers v5.22.2, no lfm3 (shallow clones searched)", "url": "https://github.com/sgl-project/sglang/tree/v0.5.23/python/sglang/srt/models", "seen": "2026-11-10" }, { "what": "Liquid's docs page for the model", "url": "https://docs.liquid.ai/lfm/models/lfm3-vl-2b", "seen": "2026-11-10" }, { "what": "Liquid's tool-use page", "url": "https://docs.liquid.ai/lfm/key-concepts/tool-use", "seen": "2026-11-10" }, { "what": "Liquid's chat-template page", "url": "https://docs.liquid.ai/lfm/key-concepts/chat-template", "seen": "2026-11-10" }, { "what": "Liquid's API model list", "url": "https://docs.liquid.ai/api/models", "seen": "2026-11-10" }, { "what": "Liquid's API text-generation guide, structured output", "url": "https://docs.liquid.ai/api/guides/text-generation", "seen": "2026-11-10" }, { "what": "Liquid's deprecations page", "url": "https://docs.liquid.ai/lfm/help/deprecations", "seen": "2026-11-10" }, { "what": "Liquid's LFM3 blog post, 27 October 2026", "url": "https://www.liquid.ai/blog/lfm3", "seen": "2026-11-10" }, { "what": "security.txt, 404", "url": "https://www.liquid.ai/.well-known/security.txt", "seen": "2026-11-10" }, { "what": "robots.txt for docs.liquid.ai and www.liquid.ai", "url": "https://docs.liquid.ai/robots.txt", "seen": "2026-11-10" } ], "openQuestions": [ "Capability is pending because no independent results were looked up on the day. The model is two weeks old", "LM Studio is credited because its catalogue runs the lfm3 architecture. If only the model's own catalogue page counts, nothing changes", "File safety takes 10 off for the repository's Python although the GGUF builds, the ones most agents will use, run none of it", "Whether Ollama's library build of this model speaks or sees as the weights can wasn't checked, only that the entry exists", "unchecked on 10 November: Liquid's licence text beyond its head, company pages and trust centre, read for the LFM2.5-2.6B listing on 21 October and 4 November, and the prices in Liquid's console" ] }, "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 }, "scoreHistory": [ { "date": "2026-11-10", "grade": "C", "kind": "listed", "method": "0.7", "score": 61 } ] }, "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" } ] }, "notable": [ "17,691 downloads in the last 30 days and 272 likes on Hugging Face on 10 November 2026, two weeks after release, and 112,199 downloads for Liquid's GGUF repository (https://huggingface.co/api/models?author=LiquidAI\u0026search=LFM3)", "The card says it scores at least 8 points ahead of every model of similar size across 62 vision benchmarks and matches Gemma 4 12B. These are Liquid's numbers, not our measurement (https://huggingface.co/LiquidAI/LFM3-VL-2B)", "Released on 27 October 2026 with the rest of the LFM3 family, which puts text, vision, audio, omni and decision models on one backbone (https://www.liquid.ai/blog/lfm3)", "Liquid's API serves it as lfm3-vl-2b (https://docs.liquid.ai/api/models)", "The tool-call format changed from LFM2.5's to a superset of qwen3_coder, and tools can be deferred until a search tool returns them (https://docs.liquid.ai/lfm/key-concepts/tool-use)", "Loading it in Transformers runs 8 Python files from the repository, since Transformers 5.22.2 has no LFM3 code of its own. The GGUF builds need none (https://huggingface.co/LiquidAI/LFM3-VL-2B/tree/main)" ], "area": "models", "details": [ { "label": "Surface graded", "value": "The open weights at huggingface.co/LiquidAI/LFM3-VL-2B and Liquid's GGUF, MLX and ONNX repositories, read from the model card, the files and their metadata, the chat template, the head of the licence, Liquid's docs and blog post, Ollama's library, LM Studio's catalogue and the runtimes' own code at their latest releases on 10 November 2026. Liquid's licence terms, company pages and security.txt were read for the LFM2.5-2.6B listing on 21 October and 4 November, and security.txt again today. Nothing was downloaded or run" }, { "label": "Model", "value": "1,918,181,888 parameters in the files (1.92B on the card: a 1.73B LFM3 language model, a 164M vision encoder and a 25M projector). The language model has 24 layers of gated short convolutions, sliding-window and global attention, and the vision encoder 14 layers with 16×16 patches at native resolution. Text, images and video in, text out. Video is sampled at 2 frames a second, 4 to 768 frames" }, { "label": "Built on", "value": "LiquidAI/LFM3-1.7B-Base, Liquid's own base model, joined with Liquid's own vision encoder, under the same LFM Open License v1.0, named on the card and in its metadata" }, { "label": "Files", "value": "Safetensors in BF16 (3.85 GB for the repository) with chat_template.jinja, config, tokenizer, a LICENSE file and 8 Python files that Transformers loads as the model's code. No pickle files. Hugging Face lists a SHA-256 for each of the 2 large files" }, { "label": "Licence", "value": "LFM Open License v1.0, Apache-like terms (copyright and patent grants, redistribution with notices) with a commercial-use limit. Commercial use is licensed only for legal entities under $10M in annual revenue, as read in full for the LFM2.5-2.6B listing. The LICENSE file is in the repository" }, { "label": "Runtimes", "value": "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. The card says SGLang support is in review and vLLM's is in progress, and neither 0.5.23 nor 0.33.0 has lfm3 code. Transformers 5.22.2 loads it only through the repository's own Python. The card asks for transformers 5.21.0 or later with trust_remote_code" }, { "label": "Context", "value": "128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. The base's mid-training reached 256K" }, { "label": "Tools and reasoning", "value": "Tools go in through `tools=` in apply_chat_template and calls come back as XML, a superset of the qwen3_coder format, which Liquid's docs name as the parser for vLLM. Tools marked `defer_loading` stay out of the prompt until a search tool returns them. 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. The card doesn't show the switch itself" }, { "label": "Hardware", "value": "No memory figure on the card. Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, its Q8_0 1.85 GB, its 4-bit MLX build 1.59 GB and the BF16 files 3.84 GB. Liquid also publishes FP8 (2.59 GB) and NVFP4 (2.21 GB) safetensors builds and an ONNX repository" }, { "label": "Training data", "value": "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. No sources, sizes by source or cut-off" }, { "label": "Languages", "value": "16 languages, listed on the card" } ], "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, "checks": [ { "check": "Legal entity named", "value": "Liquid AI, Inc. (the licensor named in the LFM Open License)", "points": 20, "max": 20, "state": "ok" }, { "check": "Domain age", "value": "liquid.ai, registered 2017-12-16 (8 years)", "points": 11, "max": 15, "state": "part" }, { "check": "Endpoint on the vendor's domain", "value": "no hosted endpoint", "points": 0, "max": 0, "state": "na" }, { "check": "Terms of service", "value": "nothing hosted, so the LFM Open License v1.0 (free commercial use below $10M annual revenue) licence stands in", "points": 10, "max": 10, "state": "ok" }, { "check": "Privacy policy", "value": "nothing hosted, not scored", "points": 0, "max": 0, "state": "na" }, { "check": "Status page", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "Changelog", "value": "published", "points": 10, "max": 10, "state": "ok" }, { "check": "security.txt", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "People named", "value": "5 named with their role, 5 with a public profile", "points": 10, "max": 10, "state": "ok" }, { "check": "Support route", "value": "help pages, a community", "points": 5, "max": 5, "state": "ok" } ] }, "humans": { "slug": "lfm3-vl-2b", "checked": "2026-11-10", "company": "Liquid AI", "people": [ { "name": "Ramin Hasani", "role": "Co-founder and CEO", "founder": true, "linkedin": "https://www.linkedin.com/in/raminhasani", "x": "https://x.com/ramin_m_h", "bio": "Ramin Hasani is co-founder and CEO of Liquid AI, spun out of MIT CSAIL, and a machine learning scientist at CSAIL. He was previously a principal AI and machine learning scientist at Vanguard and a postdoctoral associate at CSAIL with Daniela Rus. His TU Wien PhD and later research centred on liquid neural networks.", "bioSources": [ "https://www.liquid.ai/company", "https://www.raminhasani.com/pages/short-bio.html" ], "page": "/people/ramin-hasani/" }, { "name": "Mathias Lechner", "role": "Co-founder and CTO", "founder": true, "linkedin": "https://www.linkedin.com/in/mlech26l", "x": "https://x.com/mlech26l", "bio": "Mathias Lechner is co-founder and CTO of Liquid AI, which builds the LFM2 family of efficient language models, and a research affiliate at MIT CSAIL working with Daniela Rus. He completed his PhD at the Institute of Science and Technology Austria in 2022 and studied computer science at TU Wien.", "bioSources": [ "https://www.liquid.ai/company", "https://mlech26l.github.io/" ], "page": "/people/mathias-lechner/" }, { "name": "Alexander Amini", "role": "Co-founder and Chief Science Officer", "founder": true, "linkedin": "https://www.linkedin.com/in/alexanderamini", "bio": "Alexander Amini is co-founder and chief science officer of Liquid AI and a research affiliate at MIT, where he earned his PhD in computer science in 2022. His research covers autonomy and safe decision-making for autonomous agents. He also co-founded Themis AI and leads MIT's introductory deep learning course, 6.S191.", "bioSources": [ "https://www.liquid.ai/company", "https://www.mit.edu/~amini/" ], "page": "/people/alexander-amini/" }, { "name": "Daniela Rus", "role": "Co-founder", "founder": true, "linkedin": "https://www.linkedin.com/in/daniela-rus-220b3", "bio": "Daniela Rus co-founded Liquid AI, which spun out of MIT CSAIL. She is director of CSAIL and the Panasonic Professor of Computer Science at MIT, known for work in robotics and AI including distributed, soft and self-reconfiguring robots. She is a MacArthur fellow.", "bioSources": [ "https://www.liquid.ai/company", "https://en.wikipedia.org/wiki/Daniela_Rus" ], "page": "/people/daniela-rus/" }, { "name": "Tim Seyde", "role": "Post-training researcher at Liquid AI", "x": "https://x.com/timseyde", "page": "/people/tim-seyde/" } ], "peopleNote": "liquid.ai/company names the four founders on 28 October 2026, Ramin Hasani (CEO), Mathias Lechner (CTO), Alexander Amini (CSO) and Daniela Rus, and links each one's own page. It shows no team photos. 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