library_name: transformers
license: other
license_name: lfm1.0
license_link: LICENSE
pipeline_tag: text-generation
tags:

• liquid
• lfm3
• edge
• conversational
• custom_code
• Tema_Q-Abliterated
  language:
• en
• ar
• zh
• fr
• de
• it
• ja
• ko
• pt
• es
• vi
• th
• id
• hi
• ru
• pl
  base_model: LiquidAI/LFM3-1.7B

Tema_Q-Abliterated

This model was uncensored with Tema_Q-Abliterated, an exploratory weight-uncensoring (abliteration) tool for large language models. It removes refusal behavior by ablating a refusal direction from the model weights while preserving general quality (multi-position KL divergence from the base model is kept low during the search).

• Tool and source code: https://github.com/ek15072809/Tema_Q-Abliterated
• Generated with: Tema_Q-Abliterated v1.1.1 (SCAPE algorithm)
• Base model: LiquidAI/LFM3-1.7B

Usage

This model can be used exactly like the base model (transformers, vLLM, llama.cpp conversion, etc.).

To create your own uncensored model, install Tema_Q-Abliterated and run it on any HuggingFace model - a CPU-only machine is enough:

pip install temaq-abliterated
temaq-abliterated <model-name>

The SCAPE surrogate objective evaluates each search candidate about 49x faster than the full-generation evaluation used by conventional tools, so the whole uncensoring search finishes in practical time even without a GPU.

Abliteration parameters

Performance

API Try Docs Blog

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.

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.

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.

<|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>

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

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"},
    ],
})

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.

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.

GPU inference

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

Training

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.