LFM3-1.7B

← Back to Text Models LFM3-1.7B is Liquid AI’s text model for local agents and specialized applications, based on the new LFM3 tri-hybrid architecture. It is post-trained for agentic harnesses such as Hermes Agent, Pi, and OpenClaw, with a 256K-token context window. At 4-bit precision, it takes about 1 GB of memory, so it fits on any phone or laptop.

HF Base GGUF MLX ONNX FP8 NVFP4

Specifications

Property	Value
Parameters	1.73B
Context Length	256K tokens (input + output)
Architecture	LFM3 (dense), 24 layers (13 gated short convolution + 5 sliding-window attention + 6 global attention)
Vocabulary Size	128K
Languages	English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
Generation Parameters	`temperature=0.1`, `top_k=50`, `repetition_penalty=1.1`
License	LFM Open License v1.0

Local Agents

Post-trained for agent harnesses like Hermes Agent, Pi, and OpenClaw

256K Context

Long context for tool traces and multi-step workflows

Made to be Specialized

Adapt it to your domains, formats, and tools with single-GPU fine-tuning

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.

Quick Start

Transformers
llama.cpp
vLLM
SGLang

Install:

pip install -U transformers torch accelerate

Run:

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"])

Requires a llama.cpp build with LFM3 support. Use a GGUF with the current chat template, or load the template with --chat-template-file.

Install:

brew install llama.cpp

Run:

llama-cli -hf LiquidAI/LFM3-1.7B-GGUF \
    --temp 0.1 --top-k 50 --repeat-penalty 1.1

The -hf flag downloads the model directly from Hugging Face. For other installation methods and advanced usage, see the llama.cpp guide.

For local agent setup, see the Agent Harnesses guide.

Reasoning

Reasoning is enabled by default. Set enable_thinking=False in apply_chat_template() for direct answers. Reasoning from turns before the latest user message is removed unless preserve_thinking=True. See Chat Template and Tool Use for the message format. For math and science, use temperature=1.0, top_p=0.95, top_k=20, and repetition_penalty=1.0 instead of the general generation parameters.

Defer Loading and Tool Search

LFM3-1.7B supports deferred tools, so tool definitions stay out of the prompt until a search tool returns them. See Defer Loading and Tool Search for how it works and a complete example.

FP8 and NVFP4 Checkpoints

LFM3-1.7B-FP8 and LFM3-1.7B-NVFP4 are FP8 and 4-bit floating-point versions of LFM3-1.7B for GPU inference with SGLang. To use one, replace the model ID in the SGLang example above.

Models

Fine-tuning

Edge Inference

GPU Inference

Cloud Inference

Guides

Help

Specifications

Local Agents

256K Context

Made to be Specialized

Quick Start

Reasoning

Defer Loading and Tool Search

FP8 and NVFP4 Checkpoints

​Specifications

Local Agents

256K Context

Made to be Specialized

​Quick Start

​Reasoning

​Defer Loading and Tool Search

​FP8 and NVFP4 Checkpoints

Specifications

Quick Start

Reasoning

Defer Loading and Tool Search

FP8 and NVFP4 Checkpoints