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Model: LiquidAI/LFM2-2.6B-Exp Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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license: other
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license_name: lfm1.0
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license_link: LICENSE
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language:
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- en
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- ar
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- zh
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- fr
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- de
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- ja
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- ko
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- es
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pipeline_tag: text-generation
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tags:
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- liquid
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- lfm2
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- edge
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base_model:
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- LiquidAI/LFM2-2.6B-Exp
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---
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<center>
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<div style="text-align: center;">
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<img
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src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
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alt="Liquid AI"
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style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
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/>
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</div>
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<div style="display: flex; justify-content: center; gap: 0.5em;">
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<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
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</div>
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</center>
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<br>
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# LFM2-2.6B-Exp
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LFM2-2.6B-Exp is an experimental checkpoint built on [LFM2-2.6B](https://huggingface.co/LiquidAI/LFM2-2.6B) using pure reinforcement learning.
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Specifically trained on instruction following, knowledge, and math, it delivers particularly strong performance compared to other 3B models.
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In particular, its IFBench score surpasses DeepSeek R1-0528, a model 263 times larger.
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Find more information about LFM2 in our [blog post](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models).
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## 📄 Model details
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Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance.
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They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations.
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However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.
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| Property | [**LFM2-350M**](https://huggingface.co/LiquidAI/LFM2-350M) | [**LFM2-700M**](https://huggingface.co/LiquidAI/LFM2-700M) | [**LFM2-1.2B**](https://huggingface.co/LiquidAI/LFM2-1.2B) | [**LFM2-2.6B**](https://huggingface.co/LiquidAI/LFM2-2.6B) |
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| ------------------- | ----------------------------- | ----------------------------- | ----------------------------- | ----------------------------- |
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| **Parameters** | 354,483,968 | 742,489,344 | 1,170,340,608 | 2,569,272,320 |
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| **Layers** | 16 (10 conv + 6 attn) | 16 (10 conv + 6 attn) | 16 (10 conv + 6 attn) | 30 (22 conv + 8 attn) |
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| **Context length** | 32,768 tokens | 32,768 tokens | 32,768 tokens | 32,768 tokens |
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| **Vocabulary size** | 65,536 | 65,536 | 65,536 | 65,536 |
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| **Precision** | bfloat16 | bfloat16 | bfloat16 | bfloat16 |
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| **Training budget** | 10 trillion tokens | 10 trillion tokens | 10 trillion tokens | 10 trillion tokens |
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| **License** | LFM Open License v1.0 | LFM Open License v1.0 | LFM Open License v1.0 | LFM Open License v1.0
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**Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
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**Generation parameters**: We recommend the following parameters:
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* `temperature=0.3`
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* `min_p=0.15`
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* `repetition_penalty=1.05`
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**Chat template**: LFM2 uses a ChatML-like chat template as follows:
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```
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<|startoftext|><|im_start|>system
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You are a helpful assistant trained by Liquid AI.<|im_end|>
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<|im_start|>user
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What is C. elegans?<|im_end|>
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<|im_start|>assistant
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It's a tiny nematode that lives in temperate soil environments.<|im_end|>
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```
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You can automatically apply it using the dedicated [`.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#applychattemplate) function from Hugging Face transformers.
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**Tool use**: It consists of four main steps:
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1. **Function definition**: LFM2 takes JSON function definitions as input (JSON objects between `<|tool_list_start|>` and `<|tool_list_end|>` special tokens), usually in the system prompt
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2. **Function call**: LFM2 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer.
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3. **Function execution**: The function call is executed and the result is returned (string between `<|tool_response_start|>` and `<|tool_response_end|>` special tokens), as a "tool" role.
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4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.
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Here is a simple example of a conversation using tool use:
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```
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<|startoftext|><|im_start|>system
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List of tools: <|tool_list_start|>[{"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"]}}]<|tool_list_end|><|im_end|>
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<|im_start|>user
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What is the current status of candidate ID 12345?<|im_end|>
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<|im_start|>assistant
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<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
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<|im_start|>tool
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<|tool_response_start|>[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|tool_response_end|><|im_end|>
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<|im_start|>assistant
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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|>
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```
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You can directly pass tools as JSON schema or Python functions with `.apply_chat_template()` as shown in [this page](https://huggingface.co/docs/transformers/en/chat_extras) to automatically format the system prompt.
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**Architecture**: Hybrid model with multiplicative gates and short convolutions: 10 double-gated short-range LIV convolution blocks and 6 grouped query attention (GQA) blocks.
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**Pre-training mixture**: Approximately 75% English, 20% multilingual, and 5% code data sourced from the web and licensed materials.
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**Training approach**:
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* Very large-scale SFT on 50% downstream tasks, 50% general domains
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* Custom DPO with length normalization and semi-online datasets
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* Iterative model merging
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* Reinforcement learning with verifiable rewards
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## 🏃 How to run LFM2
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### 1. Transformers
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To run LFM2, you need to install Hugging Face [`transformers`](https://github.com/huggingface/transformers) v4.55 or a more recent version as follows:
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```bash
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pip install -U transformers
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```
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Here is an example of how to generate an answer with transformers in Python:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load model and tokenizer
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model_id = "LiquidAI/LFM2-2.6B-Exp"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype="bfloat16",
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# attn_implementation="flash_attention_2" <- uncomment on compatible GPU
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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# Generate answer
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prompt = "What is C. elegans?"
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input_ids = tokenizer.apply_chat_template(
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[{"role": "user", "content": prompt}],
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add_generation_prompt=True,
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return_tensors="pt",
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tokenize=True,
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).to(model.device)
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output = model.generate(
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input_ids,
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do_sample=True,
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temperature=0.3,
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min_p=0.15,
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repetition_penalty=1.05,
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max_new_tokens=512,
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)
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print(tokenizer.decode(output[0], skip_special_tokens=False))
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# <|startoftext|><|im_start|>user
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# What is C. elegans?<|im_end|>
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# <|im_start|>assistant
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# C. elegans, also known as Caenorhabditis elegans, is a small, free-living
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# nematode worm (roundworm) that belongs to the phylum Nematoda.
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```
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You can directly run and test the model with this [Colab notebook](https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing).
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### 2. vLLM
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You need to install [`vLLM`](https://github.com/vllm-project/vllm) v0.10.2 or a more recent version as follows:
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```bash
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uv pip install vllm==0.10.2 --extra-index-url https://wheels.vllm.ai/0.10.2/ --torch-backend=auto
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```
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Here is an example of how to use it for inference:
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```python
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from vllm import LLM, SamplingParams
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prompts = [
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"What is C. elegans?",
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"Say hi in JSON format",
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"Define AI in Spanish"
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]
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sampling_params = SamplingParams(temperature=0.3, min_p=0.15, repetition_penalty=1.05)
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llm = LLM(model="LiquidAI/LFM2-2.6B-Exp")
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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```
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### 3. llama.cpp
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You can run LFM2 with llama.cpp using its [GGUF checkpoint](https://huggingface.co/LiquidAI/LFM2-2.6B-Exp-GGUF). Find more information in the model card.
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## 🔧 How to fine-tune LFM2
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We recommend fine-tuning LFM2 models on your use cases to maximize performance.
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| Notebook | Description | Link |
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|-------|------|------|
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| SFT (Unsloth) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using Unsloth. | <a href="https://colab.research.google.com/drive/1HROdGaPFt1tATniBcos11-doVaH7kOI3?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| SFT (TRL) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL. | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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| DPO (TRL) | Preference alignment with Direct Preference Optimization (DPO) using TRL. | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
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## 📬 Contact
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- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
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- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
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## Citation
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```
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@article{liquidai2025lfm2,
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title={LFM2 Technical Report},
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author={Liquid AI},
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journal={arXiv preprint arXiv:2511.23404},
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year={2025}
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}
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```
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