| | --- |
| | library_name: transformers |
| | license: other |
| | license_name: lfm1.0 |
| | license_link: LICENSE |
| | language: |
| | - en |
| | - ar |
| | - zh |
| | - fr |
| | - de |
| | - ja |
| | - ko |
| | - es |
| | pipeline_tag: text-generation |
| | tags: |
| | - liquid |
| | - lfm2 |
| | - edge |
| | --- |
| | |
| | <center> |
| | <div style="text-align: center;"> |
| | <img |
| | src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" |
| | alt="Liquid AI" |
| | style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;" |
| | /> |
| | </div> |
| | <div style="display: flex; justify-content: center; gap: 0.5em;"> |
| | <a href="https://playground.liquid.ai/chat"> |
| | <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm"><strong>Documentation</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a></a> |
| | </div> |
| | </center> |
| |
|
| | # LFM2-350M |
| |
|
| | LFM2 is a new generation of hybrid models developed by [Liquid AI](https://www.liquid.ai/), specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. |
| |
|
| | We're releasing the weights of four post-trained checkpoints with 350M, 700M, 1.2B, and 2.6 parameters. They provide the following key features to create AI-powered edge applications: |
| |
|
| | * **Fast training & inference** – LFM2 achieves 3x faster training compared to its previous generation. It also benefits from 2x faster decode and prefill speed on CPU compared to Qwen3. |
| | * **Best performance** – LFM2 outperforms similarly-sized models across multiple benchmark categories, including knowledge, mathematics, instruction following, and multilingual capabilities. |
| | * **New architecture** – LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions. |
| | * **Flexible deployment** – LFM2 runs efficiently on CPU, GPU, and NPU hardware for flexible deployment on smartphones, laptops, or vehicles. |
| |
|
| | 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). |
| |
|
| | ## 📄 Model details |
| |
|
| | Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance. |
| | They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations. |
| | However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills. |
| |
|
| | | 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) | |
| | | ------------------- | ----------------------------- | ----------------------------- | ----------------------------- | ----------------------------- | |
| | | **Parameters** | 354,483,968 | 742,489,344 | 1,170,340,608 | 2,569,272,320 | |
| | | **Layers** | 16 (10 conv + 6 attn) | 16 (10 conv + 6 attn) | 16 (10 conv + 6 attn) | 30 (22 conv + 8 attn) | |
| | | **Context length** | 32,768 tokens | 32,768 tokens | 32,768 tokens | 32,768 tokens | |
| | | **Vocabulary size** | 65,536 | 65,536 | 65,536 | 65,536 | |
| | | **Precision** | bfloat16 | bfloat16 | bfloat16 | bfloat16 | |
| | | **Training budget** | 10 trillion tokens | 10 trillion tokens | 10 trillion tokens | 10 trillion tokens | |
| | | **License** | LFM Open License v1.0 | LFM Open License v1.0 | LFM Open License v1.0 | LFM Open License v1.0 | |
| |
|
| | **Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish. |
| |
|
| | **Generation parameters**: We recommend the following parameters: |
| | * `temperature=0.3` |
| | * `min_p=0.15` |
| | * `repetition_penalty=1.05` |
| |
|
| | **Chat template**: LFM2 uses a ChatML-like chat template as follows: |
| |
|
| | ``` |
| | <|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 |
| | It's a tiny nematode that lives in temperate soil environments.<|im_end|> |
| | ``` |
| |
|
| | 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. |
| |
|
| | **Tool use**: It consists of four main steps: |
| | 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 |
| | 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. |
| | 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. |
| | 4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text. |
| |
|
| | Here is a simple example of a conversation using tool use: |
| |
|
| | ``` |
| | <|startoftext|><|im_start|>system |
| | 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|> |
| | <|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 |
| | <|tool_response_start|>[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|tool_response_end|><|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|> |
| | ``` |
| |
|
| | 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. |
| |
|
| | **Architecture**: Hybrid model with multiplicative gates and short convolutions: 10 double-gated short-range LIV convolution blocks and 6 grouped query attention (GQA) blocks. |
| |
|
| | **Pre-training mixture**: Approximately 75% English, 20% multilingual, and 5% code data sourced from the web and licensed materials. |
| |
|
| | **Training approach**: |
| | * Knowledge distillation using [LFM1-7B](https://www.liquid.ai/blog/introducing-lfm-7b-setting-new-standards-for-efficient-language-models) as teacher model |
| | * Very large-scale SFT on 50% downstream tasks, 50% general domains |
| | * Custom DPO with length normalization and semi-online datasets |
| | * Iterative model merging |
| |
|
| | ## 🏃 How to run LFM2 |
| |
|
| | ### 1. Transformers |
| |
|
| | To run LFM2, you need to install Hugging Face [`transformers`](https://github.com/huggingface/transformers) v4.55 or a more recent version as follows: |
| |
|
| | ```bash |
| | pip install git+https://github.com/huggingface/transformers |
| | ``` |
| |
|
| | Here is an example of how to generate an answer with transformers in Python: |
| |
|
| | ```python |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | |
| | # Load model and tokenizer |
| | model_id = "LiquidAI/LFM2-350M" |
| | model = AutoModelForCausalLM.from_pretrained( |
| | model_id, |
| | device_map="auto", |
| | torch_dtype="bfloat16", |
| | # attn_implementation="flash_attention_2" <- uncomment on compatible GPU |
| | ) |
| | tokenizer = AutoTokenizer.from_pretrained(model_id) |
| | |
| | # Generate answer |
| | prompt = "What is C. elegans?" |
| | input_ids = tokenizer.apply_chat_template( |
| | [{"role": "user", "content": prompt}], |
| | add_generation_prompt=True, |
| | return_tensors="pt", |
| | tokenize=True, |
| | ).to(model.device) |
| | |
| | output = model.generate( |
| | input_ids, |
| | do_sample=True, |
| | temperature=0.3, |
| | min_p=0.15, |
| | repetition_penalty=1.05, |
| | max_new_tokens=512, |
| | ) |
| | |
| | print(tokenizer.decode(output[0], skip_special_tokens=False)) |
| | |
| | # <|startoftext|><|im_start|>user |
| | # What is C. elegans?<|im_end|> |
| | # <|im_start|>assistant |
| | # C. elegans, also known as Caenorhabditis elegans, is a small, free-living |
| | # nematode worm (roundworm) that belongs to the phylum Nematoda. |
| | ``` |
| |
|
| | You can directly run and test the model with this [Colab notebook](https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing). |
| |
|
| | ### 2. vLLM |
| |
|
| | You need to install [`vLLM`](https://github.com/vllm-project/vllm) v0.10.2 or a more recent version as follows: |
| |
|
| | ```bash |
| | uv pip install vllm==0.10.2 --extra-index-url https://wheels.vllm.ai/0.10.2/ --torch-backend=auto |
| | ``` |
| |
|
| | Here is an example of how to use it for inference: |
| |
|
| | ```python |
| | from vllm import LLM, SamplingParams |
| | |
| | prompts = [ |
| | "What is C. elegans?", |
| | "Say hi in JSON format", |
| | "Define AI in Spanish" |
| | ] |
| | sampling_params = SamplingParams( |
| | temperature=0.3, |
| | min_p=0.15, |
| | repetition_penalty=1.05 |
| | ) |
| | |
| | llm = LLM(model="LiquidAI/LFM2-350M") |
| | |
| | outputs = llm.generate(prompts, sampling_params) |
| | |
| | for output in outputs: |
| | prompt = output.prompt |
| | generated_text = output.outputs[0].text |
| | print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") |
| | ``` |
| |
|
| | ### 3. llama.cpp |
| |
|
| | You can run LFM2 with llama.cpp using its [GGUF checkpoint](https://huggingface.co/LiquidAI/LFM2-350M-GGUF). Find more information in the model card. |
| |
|
| | ## 🔧 How to fine-tune LFM2 |
| |
|
| | We recommend fine-tuning LFM2 models on your use cases to maximize performance. |
| |
|
| | | Notebook | Description | Link | |
| | |-------|------|------| |
| | | 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> | |
| | | 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> | |
| | | 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> | |
| |
|
| | ## 📈 Performance |
| |
|
| | LFM2 outperforms similar-sized models across different evaluation categories. |
| |
|
| | ### 1. Automated benchmarks |
| |
|
| |  |
| |
|
| | | Model | MMLU | GPQA | IFEval | IFBench | GSM8K | MGSM | MMMLU | |
| | |-------|------|------|--------|---------|-------|------|-------| |
| | | LFM2-350M | 43.43 | 27.46 | 65.12 | 16.41 | 30.1 | 29.52 | 37.99 | |
| | | LFM2-700M | 49.9 | 28.48 | 72.23 | 20.56 | 46.4 | 45.36 | 43.28 | |
| | | LFM2-1.2B | *55.23* | **31.47** | **74.89** | *20.7* | *58.3* | *55.04* | **46.73** | |
| | | Qwen3-0.6B | 44.93 | 22.14 | 64.24 | 19.75 | 36.47 | 41.28 | 30.84 | |
| | | Qwen3-1.7B | **59.11** | 27.72 | *73.98* | **21.27** | 51.4 | **66.56** | *46.51* | |
| | | Llama-3.2-1B-Instruct | 46.6 | *28.84* | 52.39 | 16.86 | 35.71 | 29.12 | 38.15 | |
| | | gemma-3-1b-it | 40.08 | 21.07 | 62.9 | 17.72 | **59.59** | 43.6 | 34.43 | |
| |
|
| | ### 2. LLM-as-a-Judge |
| |
|
| |  |
| |  |
| |
|
| | ### 3. Inference |
| |
|
| | #### Throughput comparison on CPU in ExecuTorch |
| |
|
| |  |
| |
|
| | #### Throughput comparison on CPU in Llama.cpp |
| |
|
| |  |
| |
|
| | ## 📬 Contact |
| |
|
| | If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact). |
| |
|
| | ## Citation |
| |
|
| | ``` |
| | @article{liquidai2025lfm2, |
| | title={LFM2 Technical Report}, |
| | author={Liquid AI}, |
| | journal={arXiv preprint arXiv:2511.23404}, |
| | year={2025} |
| | } |
| | ``` |
