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LFM3-1.7B

LFM3-1.7B is a text model for local agents and specialized applications, based on our new tri-hybrid architecture.

• Agentic training: post-trained for agentic harnesses such as Hermes Agent, Pi, and OpenClaw, with a 256K-token context window.
• Designed for the edge: at 4-bit precision, it takes about 1 GB of memory, so it fits on any phone or laptop.
• Made to be specialized: adapt the model to your domains, formats, and tools via quick single-GPU fine-tuning.

Find more information about the LFM3 family in our release blog post.

Model details

---

The LFM3 family includes base, text, vision, audio, omni, and decision models.

Property	Specification
Checkpoint	Post-trained, text-only language model
Parameters	1.73B
Architecture	24 layers: 13 gated short convolutions, 5 sliding-window attention, 6 global attention
Context length	256K tokens (input + output)
Vocabulary	128,000 tokens
Languages	English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
License	LFM Open License v1.0

LFM3-1.7B is designed for local agents, tool calling, structured outputs, and domain-specific fine-tuning. Use retrieval or search for knowledge-heavy tasks. It is not recommended for agentic coding or complex multi-step reasoning due to its small size.

Inference

---

Choose a checkpoint variant for your inference framework and hardware.

Checkpoint	Framework	Description
BF16	vLLM, SGLang, Transformers	BF16 weights for GPU inference.
FP8	SGLang	FP8 quantization for GPU inference.
NVFP4	SGLang	4-bit floating-point quantization for GPU inference.
GGUF	llama.cpp, Ollama, LM Studio	Local inference on CPUs and GPUs.
MLX	MLX	4-bit quantization for Apple Silicon.
ONNX	ONNX Runtime	Cross-platform inference with ONNX Runtime.

Sampling parameters:

• General (chat, tool use, agents, coding): temperature=0.1,top_k=50, repetition_penalty=1.1
• Math & STEM (math, science): temperature=1.0, top_p=0.95, top_k=20

Transformers quick start:

from transformers import pipeline

generator = pipeline(
    "text-generation",
    model="LiquidAI/LFM3-1.7B",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "What is C. elegans?"}]
output = generator(
    messages,
    do_sample=True,
    temperature=0.1,
    top_k=50,
    repetition_penalty=1.1,
    max_new_tokens=2048,
)
print(output[0]["generated_text"][-1]["content"])

For local agent setup, see the Agent Harnesses guide.

Chat template

LFM3 uses this ChatML format, generated from the checkpoint's chat template:

<|startoftext|><|im_start|>system
You are a helpful assistant trained by Liquid AI.<|im_end|>
<|im_start|>user
Explain how a heat pump works.<|im_end|>
<|im_start|>assistant
<think>

Use tokenizer.apply_chat_template() with add_generation_prompt=True to format messages automatically.

Reasoning is enabled by default. You can set enable_thinking=False to disable thinking traces, but this behavior can be inconsistent, especially for reasoning-heavy tasks.

Reasoning from turns before the latest user message is removed unless preserve_thinking=True.

Tool calling

Pass function schemas through tools=... to apply_chat_template(). The template formats tool definitions and results automatically. Generated calls use XML:

<tool_call>
<function=get_weather>
<parameter=city>
Paris
</parameter>
</function>
</tool_call>

Execute the call in your application. Add the parsed assistant call to history as a tool_calls entry, then append the result as a tool message. Keep passing the tool schemas on subsequent requests and preserve the assistant's content and reasoning from the current tool-use turn.

function.arguments must be a Python mapping. Decode JSON strings with json.loads() before applying the template.

Tool search

LFM3 supports deferred tools. Set defer_loading=True to keep tool definitions out of the prompt until your harness's search tool returns their names as tool_reference parts. The chat template then inserts their definitions into <additional_tools>.

tools = [
    search_tool,
    {"type": "function", "defer_loading": True, "function": get_weather_schema},
]
messages.append({
    "role": "tool",
    "tool_call_id": call_id,
    "content": [
        {"type": "text", "text": "Found 1 tool."},
        {"type": "tool_reference", "name": "get_weather"},
    ],
})

Your harness implements and runs the search. Keep the full tools list, including deferred tools, on every request. In the system prompt, tell the model when to search:

You only have one tool right now: search_tools, which searches a catalog of many more tools. If none of your current tools can handle a request, call search_tools to find one, then call the tool it returns.

Works with vLLM, SGLang, and LFM3-compatible llama.cpp builds. For llama.cpp, use a GGUF with the current chat template or load it with --chat-template-file.

See Deferred tools and tool search for the full guide.

Fine-tuning

---

LFM3-1.7B is designed for easy customization for a given domain, task, or application via TRL.

Training	Use case	Notebook
SFT	Extract structured JSON from messy documents
DPO	Answer from retrieved documents, or decline
GRPO	Call the right tools in a 108-tool phone app
CPT	Learn a new language (Turkish) from raw text

Performance

---

Benchmarks

LFM3-1.7B can power agentic applications that used to need 9B parameters.

Benchmark	LFM3 1.7B	LFM2.5 2.6B	MiniCPM5 2B	Gemma 4 E4B	Qwen3.5 4B	Qwen3.5 9B
Agents
LiveClaw	45.60	14.70	30.37	20.36	19.94	35.77
Claw-Eval General (EN), average	68.64	62.63	60.74	56.14	61.96	66.53
Claw-Eval General (EN), pass^3	51.40	37.52	34.58	22.43	42.99	51.87
BrowseComp-Plus (OpenClaw subset)	37.00	26.33	16.83	21.00	24.67	38.00
WebArena-Verified	38.42	3.02	9.11	17.06	21.92	28.94
EnterpriseOps-Gym	28.20	7.94	13.41	9.86	25.96	28.97
AutomationBench (public)	41.29	0.94	4.71	1.92	8.67	15.10
Tool use
BFCL V4	71.39	55.89	62.65	48.25	55.12	62.83
ToolSandbox	80.36	78.04	68.16	69.27	75.40	76.41
MCP-Atlas	32.57	16.45	26.38	6.19	41.37	42.67
τ³-bench (4 domains), average	68.01	56.77	69.44	33.53	65.98	67.11
Agentic coding
SWE-bench Verified (OpenCode)	43.40	18.00	34.80	0.50	41.20	51.20
SWE-bench Multilingual (OpenCode)	23.00	8.00	15.67	0.67	23.33	35.00
Terminal-Bench 2.1 (OpenCode)	12.92	0.00	5.62	0.00	13.48	17.98
Coding
LiveCodeBench v6	73.46	60.57	70.52	71.00	61.99	69.86
LiveCodeBench Pro 25Q2	31.74	19.16	29.34	29.94	24.25	34.43
SciCode	28.47	16.32	27.78	25.00	23.02	31.60
Instruction following
IFEval	96.66	95.39	87.83	90.02	92.52	92.08
IFBench	73.03	60.48	62.08	44.56	38.16	48.84
IFStruct	93.72	82.18	72.65	78.45	87.05	89.20
Multi-IF	89.10	84.80	73.19	81.03	80.32	84.44
Knowledge
GPQA Diamond	63.37	57.78	65.15	57.88	75.76	80.40
Humanity's Last Exam (no tools)	7.17	5.88	8.20	4.77	12.84	16.41
Humanity's Last Exam (with tools)	13.90	9.60	11.50	6.00	11.60	17.50
AA-Omniscience (public)	0.50	-33.17	-5.00	-15.33	-57.17	-28.00
AA-Omniscience accuracy (public)	7.07	8.75	9.13	9.83	15.67	13.17
AA-Omniscience non-hallucination (public)	92.93	53.69	84.44	72.09	13.64	52.59
Long context
AA-LCR	52.83	6.00	49.34	39.33	52.67	62.00
RULER 128K	65.56	1.15	37.96	18.82	84.63	87.11
Average	49.37	31.55	39.19	31.74	39.27	47.24

Evaluation details

All models were served with vLLM in BF16 at their full context length: 262,144 tokens for LFM3-1.7B, Qwen3.5-4B, and Qwen3.5-9B, and 131,072 tokens for LFM2.5-2.6B, MiniCPM5-2B, and Gemma 4 E4B. Unless listed below, every eval uses temperature 0.6 and top_p 0.95, with no output limit other than the context length. The Average row is the unweighted mean of every score in the table.

BFCL V4: greedy decoding, up to 32,768 output tokens.
ToolSandbox: greedy decoding, up to 1,024 tokens per turn.
LiveClaw: OpenClaw agent, up to 8,192 tokens per turn.
Claw-Eval: greedy decoding, 3 trials per task.
BrowseComp-Plus: OpenClaw agent, greedy decoding, up to 8,192 tokens per turn.
WebArena-Verified: up to 40 steps.
MCP-Atlas: up to 256 turns.
EnterpriseOps-Gym and AutomationBench: up to 50 turns.
SWE-bench: OpenCode agent, up to 250 turns.
Terminal-Bench 2.1: OpenCode agent, up to 100 turns.

Edge inference

At 32K context, LFM3-1.7B prefills at least 2.5x faster and decodes at least 1.8x faster than every compared model on all three devices, while using the least memory.

Evaluation details

All models run as Q4_0 GGUF files in llama.cpp with 32,768 tokens of context, batch size 1, 100 output tokens, and GPU offload. Prefill is the prompt processing speed and decode is the generation speed, both in tokens per second. LFM3-1.7B uses a llama.cpp build with LFM3 support, and the other models use llama.cpp release b11744.

AMD Ryzen AI Max+ 395: Radeon 8060S integrated GPU, Windows, Vulkan backend.
Apple M5 Max: macOS, Metal backend.
iPhone 17 Pro: Apple A19 Pro, iOS app built on llama.cpp.
Memory: peak usage in GB (10⁹ bytes). On AMD, it adds the host and GPU peaks from the same run, which can slightly overstate usage if the two peaks do not coincide. On Apple devices, the process footprint already includes Metal allocations.

GPU inference

At 1,024 concurrent requests, LFM3-1.7B can produce 3.6 billion tokens/day on one NVIDIA B300.

Training

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Pre-training: two stages build LFM3-1.7B-Base on 22T tokens in total.

• Main pre-training: compared with LFM2, the mix adds more scientific papers, repository-level code, math, and a wider range of knowledge sources.
• Mid-training: the last 3.5T tokens use curated, higher-quality data with a larger share of agentic traces and tool calls. This stage also extends the context window to 256K tokens.

Post-training: four stages develop conversation, reasoning, and tool-use capabilities.

• Supervised fine-tuning (SFT): 1.75T tokens across various tasks, including chat, reasoning, tool calling, and agentic traces.
• Mixed reinforcement learning: many domains trained at once, from math and code to SQL and structured outputs. A length penalty in the reward keeps answers concise.
• Multi-domain on-policy distillation (MOPD): six specialist RL teachers (code, math, tool use, long context, agentic work, and enterprise workflows) are distilled back into a single model.
• Agentic RL: tasks from web browsing to office work are run inside real agent harnesses, preparing the model for deployment.

Citation

---

@article{liquidAI2026LFM3,
  author  = {Liquid AI},
  title   = {Introducing LFM3: Every Modality for Specialized Intelligence},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm3},
}

For questions and feedback, join Discord. For custom deployment and fine-tuning support, contact Liquid AI.