{ "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-omni-2b", "name": "LFM3-Omni-2B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM3-Omni-2B is Liquid AI's open-weight 2.17B omni model for on-device use, taking text, images, video and audio and answering in text or speech, with tool calling and a 32K-token context, released on 27 October 2026 with GGUF and MLX builds.", "url": "https://www.anchorterminal.com/tools/lfm3-omni-2b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-omni-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-omni-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-omni-2b.json", "dns": "lfm3-omni-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B-MLX-4bit" }, { "registry": "ollama", "name": "lfm3-omni" } ], "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 0.29 GB projector file and 4.49 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-omni-2b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "omni", "vision", "video", "audio", "speech", "small", "edge", "tool-calling", "custom-licence", "safetensors", "gguf", "mlx", "onnx", "self-hosted" ], "lastRelease": "2026-11-03", "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": 55, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 80, "risk": 40, "upkeep": 49 }, "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": 80, "points": 30.77, "reason": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 vision and audio projectors and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example), and Ollama's library has an lfm3-omni entry. 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-omni-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.", "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": 70, "points": 10.77, "reason": "Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results. 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 documents tool calling from spoken or written requests. Structured output 0, the card doesn't mention JSON or structured output for this model. Reasoning control 0, the card says this is a non-reasoning model and that `enable_thinking=True` is not supported, so there is no thinking to turn off or budget. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 0 + 0 + 5 = 70.", "checked": "2026-11-10" }, { "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "effectiveWeight": 15.38, "score": 49, "points": 7.54, "reason": "Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B), context (32K) and the text and speech languages. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 28 October, documented video frame rate and frame limits. Responsiveness 0, none of the 12 discussions that aren't pull requests (27 October to 9 November 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 14 + 5 + 20 + 0 + 5 + 5 = 49.", "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 (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results. 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 documents tool calling from spoken or written requests. Structured output 0, the card doesn't mention JSON or structured output for this model. Reasoning control 0, the card says this is a non-reasoning model and that `enable_thinking=True` is not supported, so there is no thinking to turn off or budget. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 0 + 0 + 5 = 70.", "reach": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 vision and audio projectors and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example), and Ollama's library has an lfm3-omni entry. 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-omni-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.", "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 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B), context (32K) and the text and speech languages. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 28 October, documented video frame rate and frame limits. Responsiveness 0, none of the 12 discussions that aren't pull requests (27 October to 9 November 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 14 + 5 + 20 + 0 + 5 + 5 = 49." }, "sources": [ { "what": "model card", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "seen": "2026-11-10" }, { "what": "gating, dates, parameter count, downloads, likes, file list and hashes", "url": "https://huggingface.co/api/models/LiquidAI/LFM3-Omni-2B?blobs=true", "seen": "2026-11-10" }, { "what": "inference providers, none mapped", "url": "https://huggingface.co/api/models/LiquidAI/LFM3-Omni-2B?expand[]=inferenceProviderMapping", "seen": "2026-11-10" }, { "what": "commit history", "url": "https://huggingface.co/api/models/LiquidAI/LFM3-Omni-2B/commits/main", "seen": "2026-11-10" }, { "what": "architecture, auto_map and context length", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B/raw/main/config.json", "seen": "2026-11-10" }, { "what": "chat template", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B/raw/main/chat_template.jinja", "seen": "2026-11-10" }, { "what": "licence, head read", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-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-Omni-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-Omni-2B-GGUF?blobs=true", "seen": "2026-11-10" }, { "what": "Liquid's 4-bit MLX build", "url": "https://huggingface.co/api/models/LiquidAI/LFM3-Omni-2B-MLX-4bit?blobs=true", "seen": "2026-11-10" }, { "what": "Ollama's library tags", "url": "https://ollama.com/library/lfm3-omni/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-omni-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 not credited because its catalogue has no entry for this model, though it runs the lfm3 architecture for LFM3-VL-2B", "File safety takes 10 off for the repository's Python although the GGUF builds, the ones most agents will use, run none of it", "Context is 5 on the card's 32K although config.json and Liquid's API both say 128,000. If the card is out of date, Context is 10 and File health 10", "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": "One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results or data sources.", "bestFor": "Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools", "strengths": [ "One model for text, images, video, audio and speech on an 8 GB machine", "Speech output and a realtime speech server in released llama.cpp", "Tool calls from spoken requests, in a documented format", "Updated a week before this check", "Liquid's own API serves it" ], "weaknesses": [ "Only llama.cpp and Ollama run it in a release", "A 32K-token context, which video and audio fill quickly", "It doesn't reason, and structured output isn't documented", "None of 12 questions on the repository has a reply from Liquid", "Loading in Transformers runs 14 Python files from the repository", "No safety results, security contact or training data description" ], "agentNotes": [ "Start it with `llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-omni`", "Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card", "The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames", "Parse tool calls yourself from the XML it returns", "It doesn't reason, so don't pass enable_thinking", "Validate JSON output yourself" ], "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": 70, "reach": 80, "risk": 40, "upkeep": 49 }, "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-Omni-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-omni", "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-omni-2b" }, "sameCompany": [ "liquid-d1", "lfm2-5-2-6b", "lfm3-1-7b", "lfm3-audio-2b", "lfm3-vl-2b" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "The LFM3 backbone (Liquid's own; the metadata names no base model)", "params": "2.17B", "context": 32000, "goodFor": "One small on-device model in place of separate speech recognition, vision, language and speech models, for voice chat, questions about video with its soundtrack and tool calls from spoken requests. It doesn't reason.", "runtimes": [ "llama.cpp", "Ollama" ], "builds": [ { "name": "LFM3-Omni-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "who": "publisher", "precision": "bf16", "sizeGB": 4.49, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ], "note": "Loads in Transformers with the repository's own code. Liquid also publishes an ONNX repository. It has no FP8 or NVFP4 build of this model" }, { "name": "LFM3-Omni-2B-GGUF Q8_0", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 2.36, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "LFM3-Omni-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-Omni-2B-Q8_0.gguf (0.51 GB)" }, { "name": "LFM3-Omni-2B-GGUF Q4_K_M", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.39, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "LFM3-Omni-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-Omni-2B-Q4_K_M.gguf (0.29 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files" }, { "name": "LFM3-Omni-2B-MLX-4bit", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 2.11, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX-VLM" ], "note": "Liquid also publishes 8-bit and BF16 MLX builds" }, { "name": "ollama lfm3-omni", "url": "https://ollama.com/library/lfm3-omni", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2, "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-Omni-2B-GGUF:Q4_K_M --jinja", "runBuild": "LFM3-Omni-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-VL-2B", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B", "params": "1.92B", "listing": "https://www.anchorterminal.com/tools/lfm3-vl-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": [ "18,100 downloads in the last 30 days and 436 likes on Hugging Face on 10 November 2026, two weeks after release, and 341,709 downloads for Liquid's GGUF repository (https://huggingface.co/api/models?author=LiquidAI\u0026search=LFM3)", "The card reports omni, vision and audio benchmark tables against other small models. These are Liquid's numbers, not our measurement (https://huggingface.co/LiquidAI/LFM3-Omni-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-omni-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 14 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-Omni-2B/tree/main)" ], "area": "models", "details": [ { "label": "Surface graded", "value": "The open weights at huggingface.co/LiquidAI/LFM3-Omni-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": "2,166,928,384 parameters in the files (2.17B on the card): the LFM3 backbone, Liquid's vision encoder, a causal audio encoder and the LiquidCodec audio tokenizer. Text, images, video with or without its soundtrack and 16 kHz audio in; text, 24 kHz speech or the two interleaved out. Video is sampled at 2 frames a second, 4 to 384 frames. It doesn't reason" }, { "label": "Built on", "value": "The LFM3 backbone, per the card. The repository's metadata names no base model, and the licence is the same LFM Open License v1.0" }, { "label": "Files", "value": "Safetensors in BF16 (4.50 GB for the repository) with chat_template.jinja, config, tokenizer, a LICENSE file and 14 Python files that Transformers loads as the model's code. No pickle files. Hugging Face lists a SHA-256 for each of the 5 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 with the LFM3 vision and audio projectors and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example), and Ollama's library has an lfm3-omni entry. The card names SGLang for text output, and SGLang 0.5.23 has no lfm3 code. vLLM 0.33.0 has none, Transformers 5.22.2 loads it only through the repository's own Python, and LM Studio's catalogue has no entry for it. The card asks for transformers 5.20.2 or later with trust_remote_code" }, { "label": "Context", "value": "32K tokens of input and output per the card. config.json gives the language model 128,000 positions and Liquid's API lists the hosted model at 128,000, but the card's figure is the one counted" }, { "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. The card says this is a non-reasoning model and that `enable_thinking=True` is not supported, so there is no thinking to turn off or budget" }, { "label": "Hardware", "value": "No memory figure on the card. Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, its Q8_0 1.85 GB, its 4-bit MLX build 2.11 GB and the BF16 files 4.49 GB. Liquid also publishes an ONNX repository. It has no FP8 or NVFP4 build of this model" }, { "label": "Training data", "value": "The card has no training section. Liquid's blog post describes the shared LFM3 backbone's 22T tokens of pre-training in general terms and says nothing about the audio, vision or speech data this model was trained on. No sources or cut-off" }, { "label": "Languages", "value": "16 text languages and 19 speech languages and locales, 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-Omni-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-omni-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. The d1 launch post is signed Liquid AI and the company news posts name no product leads.", "companySocial": { "linkedin": "https://www.linkedin.com/company/liquid-ai-inc/", "x": "https://x.com/liquidai" }, "support": { "urls": [ "https://docs.liquid.ai/lfm/models/lfm3-omni-2b" ], "community": [ { "label": "Discord", "url": "https://discord.gg/DFU3WQeaYD", "kind": "discord" }, { "url": "https://discord.com/invite/liquid-ai", "kind": "discord" } ] }, "sources": [ "https://www.liquid.ai/company", "https://www.raminhasani.com/#home", "https://www.liquid.ai/blog/d1-decision-model", "https://www.linkedin.com/in/raminhasani", "https://twitter.com/ramin_m_h", "https://mlech26l.github.io/", "https://www.linkedin.com/in/mlech26l", "https://x.com/mlech26l", "https://www.mit.edu/~amini/", "https://www.linkedin.com/in/alexanderamini", "https://www.linkedin.com/in/daniela-rus-220b3", "https://www.liquid.ai/blog/lfm2-5-2-6b", "https://tseyde.github.io", "https://x.com/timseyde", "https://www.liquid.ai/", "https://www.liquid.ai", "https://www.liquid.ai/connect", "https://docs.liquid.ai/lfm/models/lfm3-omni-2b" ], "ownership": { "status": "private", "checked": "2026-10-29", "sources": [ "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "https://www.liquid.ai/blog/we-raised-250m-to-scale-capable-and-efficient-general-purpose-ai", "https://www.maginative.com/article/amd-leads-250m-investment-in-liquid-ai-betting-on-worm-inspired-ai-architecture/", "https://www.liquid.ai/company" ] }, "investors": [ { "name": "AMD Ventures", "firm": "AMD", "kind": "corporate", "rounds": [ "Series A, December 2024" ], "lead": true, "url": "https://www.amd.com", "source": "https://www.liquid.ai/blog/we-raised-250m-to-scale-capable-and-efficient-general-purpose-ai", "quote": "We are pleased to announce our Series A round of financing with AMD Ventures as strategic lead.", "page": "/investors/amd/" }, { "name": "OSS Capital", "kind": "vc", "rounds": [ "Seed, December 2023", "Series A, December 2024" ], "lead": true, "url": "https://oss.capital", "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/oss-capital/" }, { "name": "PagsGroup", "kind": "other", "rounds": [ "Seed, December 2023", "Series A, December 2024" ], "lead": true, "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/pagsgroup/" }, { "name": "Duke Capital Partners", "kind": "vc", "rounds": [ "Seed, December 2023", "Series A, December 2024" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/duke-capital-partners/" }, { "name": "Breyer Capital", "kind": "vc", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/breyer-capital/" }, { "name": "Safar Partners", "kind": "vc", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/safar-partners/" }, { "name": "ISAI Cap Venture", "firm": "ISAI", "kind": "corporate", "rounds": [ "Seed, December 2023" ], "url": "https://www.isai.fr", "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/isai/" }, { "name": "Samsung Next", "firm": "Samsung", "kind": "corporate", "rounds": [ "Seed, December 2023" ], "url": "https://www.samsungnext.com", "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/samsung/" }, { "name": "Link Ventures", "kind": "vc", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/link-ventures/" }, { "name": "Bold Capital Partners", "kind": "vc", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/bold-capital-partners/" }, { "name": "Argean Capital Management", "kind": "vc", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/argean-capital-management/" }, { "name": "Latham \u0026 Watkins", "kind": "other", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/latham-watkins/" }, { "name": "Automattic", "kind": "corporate", "rounds": [ "Seed, December 2023" ], "url": "https://automattic.com", "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/automattic/" }, { "name": "Tom Preston-Werner", "kind": "angel", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/tom-preston-werner/" }, { "name": "Naval Ravikant", "kind": "angel", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/naval-ravikant/" }, { "name": "Tobias Lütke", "kind": "angel", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/tobias-lutke/" }, { "name": "David Siegel", "kind": "angel", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/david-siegel/" }, { "name": "Chris Prucha", "kind": "angel", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/chris-prucha/" }, { "name": "David Blundin", "kind": "angel", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/david-blundin/" }, { "name": "Marc Casper", "kind": "angel", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/marc-casper/" }, { "name": "Bob Young", "kind": "angel", "rounds": [ "Seed, December 2023" ], "source": "https://www.liquid.ai/blog/new-generation-of-ai-models-from-first-principles", "page": "/investors/bob-young/" } ] }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm3-omni-2b.json" } }