--- 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 ---

Try LFM • Documentation • LEAP

# LFM2-1.2B 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 -U 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/LFM2-1.2B" 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/LFM2-1.2B") 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/LFM2-1.2B-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. |

| | SFT (TRL) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL. |

| | DPO (TRL) | Preference alignment with Direct Preference Optimization (DPO) using TRL. |

| ## 📈 Performance LFM2 outperforms similar-sized models across different evaluation categories. ### 1. Automated benchmarks ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/3cB7VqMnrG9I8EqrL7k-q.png) | 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 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/4Yxx0l9aQ6ATrps5GWHzv.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/lzpZOGwH-8bTlOWd3tv6M.png) ### 3. Inference #### Throughput comparison on CPU in ExecuTorch ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/KoKcsXUOnkvz2dwZ99k08.png) #### Throughput comparison on CPU in Llama.cpp ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/c7UYZ5nh6qJMB4rd6WKde.png) ## 📬 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} } ```

LiquidAI/LFM2-1.2B

Deploy Model on Runcrate

Run LFM2-1.2B on Runcrate