LiquidAI/LFM2.5-2.6B · Hugging Face

Source: https://huggingface.co/LiquidAI/LFM2.5-2.6B

LFM2.5-2.6B is part of LFM2.5, a family of hybrid models designed foron-device deployment. It builds on the LFM2 architecture with a 128K context window and agentic post-training.

• Best-in-class agent: Competitive with models 4x larger on tool use, instruction following, and multi-step agentic tasks.
• Agentic reinforcement learning: Trained inside the most popular agentic harnesses to improve compatibility.
• Efficient inference: 220 tok/s on an Apple M5 Max and 113 tok/s on an AMD Ryzen CPU, in under 2.5 GB of memory.

Find more information about LFM2.5-2.6B in ourblog post.

💻Demos: Try LFM2.5-2.6B’s agentic capabilities in a Hugging Face space without any setup:Research Agent in your browser: helps you research a specific question and generates a summary

https://huggingface.co/LiquidAI/LFM2.5-2.6B#%F0%9F%97%92%EF%B8%8F-model-details🗒️ Model Details

ModelParametersDescriptionLFM2.5-2.6B-Base2.6BPre-trained base model for fine-tuning**LFM2.5-2.6B**2.6BPost-trained for agentic workloads LFM2.5-2.6B is a general-purpose text-only model with the following features:

• Total parameters: 2.69B
• Number of layers: 30 (22 double-gated short convolution blocks + 8 GQA)
• Training budget: 34 trillion tokens
• Vocabulary size: 128,000
• Context length: 131,072 tokens
• 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

ModelDescription**LFM2.5-2.6BOriginal model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang.LFM2.5-2.6B-GGUFQuantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage.LFM2.5-2.6B-ONNXONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile).LFM2.5-2.6B-MLX**MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework. We recommend using it for agentic workloads, tool use, data extraction, RAG, and long-context workflows. It is not recommended for agentic coding and knowledge-heavy tasks.

https://huggingface.co/LiquidAI/LFM2.5-2.6B#chat-templateChat Template

LFM2.5 uses a ChatML-like format. See theChat Template documentationfor details. Example:

<|startoftext|><|im_start|>system
You are a helpful assistant trained by Liquid AI.<|im_end|>
<|im_start|>user
What is C. elegans?<|im_end|>
<|im_start|>assistant

You can usetokenizer\.apply\_chat\_template\(\)to format your messages automatically.

💡Note: LFM2.5-2.6B is a pure reasoning models that always thinks before it answers. It adds a<think\>tag directly in thechat templatewhen starting an assistant answer.

https://huggingface.co/LiquidAI/LFM2.5-2.6B#tool-useTool Use

LFM2.5 supports function calling in four steps:

1. Function definition: Provide the list of tools as a JSON object in the system prompt, or usetokenizer\.apply\_chat\_template\(\)withtools=\.\.\..
2. Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between<\|tool\_call\_start\|\>and<\|tool\_call\_end\|\>special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
3. Function execution: Execute the call and return the result with thetoolrole.
4. Final answer: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt.

See theTool Use documentationfor the full guide. Example:

<|startoftext|><|im_start|>system
List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|im_end|>
<|im_start|>user
What is the current status of candidate ID 12345?<|im_end|>
<|im_start|>assistant
<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
<|im_start|>tool
[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|>
<|im_start|>assistant
The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>

https://huggingface.co/LiquidAI/LFM2.5-2.6B#trainingTraining

LFM2.5-2.6B is pre-trained on ~34T tokens, with a mid-training phase that extends the context window to 128K. Post-training then turns the base model into an agent in four stages: supervised fine-tuning (two rounds), per-domain teacher specialization, multi-domain on-policy distillation, and agentic reinforcement learning.

In particular, agentic reinforcement learning allows us to directly train the model inside popular agentic harnesses. It exposes the model to their tools, system prompts, and interaction patterns, helping it work reliably across agent environments.

https://huggingface.co/LiquidAI/LFM2.5-2.6B#%F0%9F%8F%83-inference🏃 Inference

LFM2.5 is supported by many inference frameworks. See theInference documentationfor the full list.

Quick start with Transformers (compatible withtransformers\>=5\.0\.0):

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-2.6B"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
#   attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "What is C. elegans?"

input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
)["input_ids"].to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.1,
    top_k=50,
    repetition_penalty=1.1,
    max_new_tokens=512,
    streamer=streamer,
)

https://huggingface.co/LiquidAI/LFM2.5-2.6B#%F0%9F%94%A7-fine-tuning🔧 Fine-Tuning

We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.

https://huggingface.co/LiquidAI/LFM2.5-2.6B#%F0%9F%93%8A-performance📊 Performance

https://huggingface.co/LiquidAI/LFM2.5-2.6B#benchmarksBenchmarks

We compared LFM2.5-2.6B with relevant sub-10B models on a diverse suite of benchmarks.

BenchmarkLFM2.5-2.6B (2.6B)gemma-4-E2B-it (5.1B)gemma-4-E4B-it (8B)Qwen3.5-4B (4.7B)Qwen3.5-9B (9.7B)AA Omniscience-29.50-74.47-49.03-54.30-50.43AIME2551.8726.3334.2749.3356.07LiveCodeBenchv659.4154.9263.7760.8569.86IFBench59.1734.0839.2448.4056.47Multi-IF80.0769.4477.3555.6762.55IFStruct85.4964.8576.6536.2578.50BFCLv456.8836.9846.3950.5660.13ToolSandbox77.8352.4065.0075.5576.44τ³-Bench Banking5.673.354.125.455.15Claw-Eval average (EN)62.8553.1458.0262.2866.53PinchBench68.2244.2455.0971.2671.45BrowseComp+ (OpenClaw)26.898.3115.9024.4627.23

https://huggingface.co/LiquidAI/LFM2.5-2.6B#cpu-inferenceCPU Inference

Due to its efficient LFM2 architecture, LFM2.5-2.6B is the fastest model we tested, with decode speeds of 220 tokens/s on an M5 Max and 113 tokens/s on a Ryzen AI Max+ 395. At 30 tokens/s, it allows you to run capable agents even on a phone.

https://huggingface.co/LiquidAI/LFM2.5-2.6B#gpu-inferenceGPU Inference

LFM2.5-2.6B is the fastest model in its size class, reaching almost15K output tokens per second at high concurrency, roughly 1.3B tokens per day on a single H100.

https://huggingface.co/LiquidAI/LFM2.5-2.6B#%F0%9F%93%AC-contact📬 Contact

• Got questions or want to connect?Join our Discord community
• If you are interested in custom solutions with edge deployment, please contactour sales team.

https://huggingface.co/LiquidAI/LFM2.5-2.6B#citationCitation

@article{liquidAI202626B,
  author  = {Liquid AI},
  title   = {LFM2.5-2.6B: Agents Everywhere},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm2-5-2-6b},
}

@article{liquidai2025lfm2,
  title   = {LFM2 Technical Report},
  author  = {Liquid AI},
  journal = {arXiv preprint arXiv:2511.23404},
  year    = {2025}
}