LFM3-1.7B
설명
LFM3-1.7B is Liquid AI's open-weight 1.73B text model for local agents and specialized applications, the text post-trained checkpoint of the LFM3 family (Oct 27, 2026). New tri-hybrid architecture: 24 layers (13 gated short convolutions, 5 sliding-window attention with 256-token window, 6 global attention), head-wise gated attention, partial RoPE, sandwich norm. Pre-trained on 22T tokens; 256K context; 128K vocabulary; 16 languages. Post-trained (SFT, mixed RL, multi-domain on-policy distillation, agentic RL) for harnesses like Hermes Agent, Pi, and OpenClaw; reasoning on by default; supports deferred tools / tool search. Fits in ~1 GB at 4-bit. Vendor says it is not recommended for agentic coding or complex multi-step reasoning. Hugging Face: LiquidAI/LFM3-1.7B (+ Base, FP8, NVFP4, GGUF, MLX, ONNX). License: LFM Open License v1.0. Catalog benches are self-reported from the vendor table. No catalog id (recorded here only): LiveClaw 45.60, Claw-Eval pass^3 51.40, BrowseComp-Plus (OpenClaw subset) 37.00, EnterpriseOps-Gym 28.20, ToolSandbox 80.36, IFStruct 93.72, LiveCodeBench Pro 25Q2 31.74 (pass rate; catalog id is Elo), AA-Omniscience accuracy 7.07 and non-hallucination 92.93. Launch partners include OpenRouter and Vercel, but no gateway id or price was listed at time of adding, so no provider row. Sources: https://www.liquid.ai/blog/lfm3 · https://huggingface.co/LiquidAI/LFM3-1.7B.
능력 레이더
전용 과학 벤치마크가 없을 때 Science는 LLM Stats 과학 점수 또는 추론 능력에서 추정합니다.
랭킹
| 도메인 | #순위 | 점수 | 소스 |
|---|---|---|---|
| 에이전트형 역량 | 34 | 59.0 | LS |
벤치마크 점수 (LLM Stats)
(LLM Stats (zeroeval))Agents
Chat
Code
Instruction Following
Knowledge
Long Context
Reasoning
Tool Calling
AA 평가 지수
(Artificial Analysis)AA 평가 데이터가 없습니다
LLM Stats 카테고리 점수
(LLM Stats (zeroeval))가격
속도
속도 데이터가 없습니다
공급자 가격 순위
공급자 가격 순위
4개 공급자
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