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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: LiquidAI/LFM2-1.2B |
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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/chat"> |
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<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> |
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</div> |
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</center> |
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# LFM2-1.2B-Tool |
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Based on [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B), LFM2-1.2B-Tool is designed for **concise and precise tool calling**. The key challenge was designing a non-thinking model that outperforms similarly sized thinking models for tool use. |
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**Use cases**: |
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- Mobile and edge devices requiring instant API calls, database queries, or system integrations without cloud dependency. |
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- Real-time assistants in cars, IoT devices, or customer support, where response latency is critical. |
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- Resource-constrained environments like embedded systems or battery-powered devices needing efficient tool execution. |
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You can find more information about other task-specific models in this [blog post](https://www.liquid.ai/blog/introducing-liquid-nanos-frontier-grade-performance-on-everyday-devices). |
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## 📄 Model details |
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**Generation parameters**: We recommend using greedy decoding with a `temperature=0`. |
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**System prompt**: The system prompt must provide all the available tools |
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**Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish. |
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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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> [!WARNING] |
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> ⚠️ The model supports both single-turn and multi-turn conversations. |
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## 📈 Performance |
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For edge inference, latency is a crucial factor in delivering a seamless and satisfactory user experience. Consequently, while test-time-compute inherently provides more accuracy, it ultimately compromises the user experience due to increased waiting times for function calls. |
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Therefore, the goal was to develop a tool calling model that is competitive with thinking models, yet operates without any internal chain-of-thought process. |
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We evaluated each model on a proprietary benchmark that was specifically designed to prevent data contamination. The benchmark ensures that performance metrics reflect genuine tool-calling capabilities rather than memorized patterns from training data. |
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## 🏃 How to run |
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- Hugging Face: [LFM2-1.2B-Tool](https://huggingface.co/LiquidAI/LFM2-1.2B-Tool) |
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- llama.cpp: [LFM2-1.2B-Tool-GGUF](https://huggingface.co/LiquidAI/LFM2-1.2B-Tool-GGUF) |
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- LEAP: [LEAP model library](https://leap.liquid.ai/models?model=lfm2-1.2b-tool) |
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You can use the following Colab notebooks for easy inference and fine-tuning: |
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| Notebook | Description | Link | |
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|-------|------|------| |
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| Inference | Run the model with Hugging Face's transformers library. | <a href="https://colab.research.google.com/drive/1_HFBuNROTnI-SSZ2zEpqpjJ6SnrsWCU3?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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| 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 (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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## 📬 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). |
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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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``` |
