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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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--- |
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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/61b8e2ba285851687028d395/7_6D7rWrLxp2hb6OHSV1p.png" |
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alt="Liquid AI" |
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style="width: 100%; max-width: 66%; 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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<a href="https://playground.liquid.ai/chat"> |
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<svg width="114.8" height="20" viewBox="0 0 1300 200" xmlns="http://www.w3.org/2000/svg" role="img" aria-label="Liquid Playground" style="margin-bottom: 1em;"> |
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<title>Liquid: Playground</title> |
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<g> |
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<rect fill="#fff" width="600" height="200"></rect> |
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<rect fill="url(#x)" x="600" width="700" height="200"></rect> |
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</g> |
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<g transform="translate(20, 30) scale(0.4, 0.4)"> |
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<path d="M172.314 129.313L172.219 129.367L206.125 188.18C210.671 195.154 213.324 203.457 213.324 212.382C213.324 220.834 210.956 228.739 206.839 235.479L275.924 213.178L167.853 33.6L141.827 76.9614L172.314 129.313Z" fill="black"/> |
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<path d="M114.217 302.4L168.492 257.003C168.447 257.003 168.397 257.003 168.352 257.003C143.515 257.003 123.385 237.027 123.385 212.387C123.385 203.487 126.023 195.204 130.55 188.24L162.621 132.503L135.966 86.7327L60.0762 213.183L114.127 302.4H114.217Z" fill="black"/> |
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<path d="M191.435 250.681C191.435 250.681 191.43 250.681 191.425 250.686L129.71 302.4H221.294L267.71 226.593L191.435 250.686V250.681Z" fill="black"/> |
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</g> |
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<g aria-hidden="true" fill="#fff" text-anchor="start" font-family="Verdana,DejaVu Sans,sans-serif" font-size="110"> |
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<text x="200" y="148" textLength="329" fill="#000" opacity="0.1">Liquid</text> |
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<text x="190" y="138" textLength="329" fill="#000">Liquid</text> |
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<text x="655" y="148" textLength="619" fill="#000" opacity="0.1">Playground</text> |
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<text x="645" y="138" textLength="619">Playground</text> |
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</g> |
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<linearGradient id="x" x1="0%" y1="0%" x2="100%" y2="0%"> |
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<stop offset="0%" style="stop-color:#000000"></stop> |
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<stop offset="100%" style="stop-color:#000000"></stop> |
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</linearGradient> |
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</svg> |
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</a> |
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</center> |
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# LFM2-1.2B |
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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. |
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We're releasing the weights of three post-trained checkpoints with 350M, 700M, and 1.2B parameters. They provide the following key features to create AI-powered edge applications: |
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* **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. |
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* **Best performance** – LFM2 outperforms similarly-sized models across multiple benchmark categories, including knowledge, mathematics, instruction following, and multilingual capabilities. |
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* **New architecture** – LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions. |
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* **Flexible deployment** – LFM2 runs efficiently on CPU, GPU, and NPU hardware for flexible deployment on smartphones, laptops, or vehicles. |
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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 | Value | |
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| ------------------- | ----------------------------- | |
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| **Parameters** | 1,170,340,608 | |
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| **Layers** | 16 (10 conv + 6 attn) | |
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| **Context length** | 32,768 tokens | |
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| **Vocabulary size** | 65,536 | |
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| **Precision** | bfloat16 | |
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| **Training budget** | 10 trillion tokens | |
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| **License** | 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 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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**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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* Knowledge distillation using [LFM1-7B](https://www.liquid.ai/blog/introducing-lfm-7b-setting-new-standards-for-efficient-language-models) as teacher model |
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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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## 🏃 How to run LFM2 |
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You can run LFM2 with transformers and llama.cpp. vLLM support is coming. |
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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 more recent as follows: |
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```python |
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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/LFM2-1.2B" |
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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. Llama.cpp |
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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. |
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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/github/unslothai/notebooks/blob/main/nb/Liquid_LFM2_(1.2B)-Conversational.ipynb"><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 (Axolotl) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using Axolotl. | <a href="https://colab.research.google.com/drive/155lr5-uYsOJmZfO6_QZPjbs8hA_v8S7t?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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## 📈 Performance |
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LFM2 outperforms similar-sized models across different evaluation categories. |
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### 1. Automated benchmarks |
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| Model | MMLU | GPQA | IFEval | IFBench | GSM8K | MGSM | MMMLU | |
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|-------|------|------|--------|---------|-------|------|-------| |
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| LFM2-350M | 43.43 | 27.46 | 65.12 | 16.41 | 30.1 | 29.52 | 37.99 | |
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| LFM2-700M | 49.9 | 28.48 | 72.23 | 20.56 | 46.4 | 45.36 | 43.28 | |
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| LFM2-1.2B | *55.23* | **31.47** | **74.89** | *20.7* | *58.3* | *55.04* | **46.73** | |
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| Qwen3-0.6B | 44.93 | 22.14 | 64.24 | 19.75 | 36.47 | 41.28 | 30.84 | |
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| Qwen3-1.7B | **59.11** | 27.72 | *73.98* | **21.27** | 51.4 | **66.56** | *46.51* | |
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| Llama-3.2-1B-Instruct | 46.6 | *28.84* | 52.39 | 16.86 | 35.71 | 29.12 | 38.15 | |
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| gemma-3-1b-it | 40.08 | 21.07 | 62.9 | 17.72 | **59.59** | 43.6 | 34.43 | |
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### 2. LLM-as-a-Judge |
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### 3. Inference |
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#### Throughput comparison on CPU in ExecuTorch |
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#### Throughput comparison on CPU in Llama.cpp |
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## 📬 Contact |
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If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact). |
