Playground

--- library_name: transformers license: other license_name: lfm1.0 license_link: LICENSE language: - en pipeline_tag: text-generation tags: - liquid - lfm2 - edge base_model: LiquidAI/LFM2-350M ---

# LFM2-350M-Math Based on [LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M), LFM2-350M-Math is a tiny reasoning model designed for tackling tricky math problems. 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). ## 📄 Model details **Generation parameters**: We strongly recommend using greedy decoding with a `temperature=0.6`, `top_p=0.95`, `min_p=0.1`, `repetition_penalty=1.05`. **System prompt**: We recommend not using any system prompt. **Supported languages**: English only. **Chat template**: LFM2 uses a ChatML-like chat template as follows: ``` <|startoftext|><|im_start|>user Find the sum of all integer bases $b>9$ for which $17_{b}$ is a divisor of $97_{b}$.<|im_end|> <|im_start|>assistant <|cot_start|>First, we need to convert $17_{b}$ and $97_{b}$ into base 10. [...]<|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. > [!WARNING] > ⚠️ The model is intended for single-turn conversations. ## 📈 Performance Reasoning enables models to better structure their thought process, explore multiple solution strategies, and self-verify their final responses. Augmenting tiny models with extensive test-time compute in this way allows them to even solve challenging competition-level math problems. Our benchmark evaluations demonstrate that LFM2-350M-Math is highly capable for its size. ![68d41660ccb9b4bb78d0ad93_Response Accuracy - dark mode](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/NTZ7lZPY1CAaSm73zCKHO.png) As we are excited about edge deployment, our goal is to limit memory consumption and latency. Our post-training recipe leverages reinforcement learning to explicitly bring down response verbosity where it is not desirable. To this end, we combine explicit reasoning budgets with difficulty-aware advantage re-weighting. Please refer to our separate [blog post](https://www.liquid.ai/research/lfm-1b-math-can-small-models-be-concise-reasoners) for a detailed post-training recipe. ![68d4166ef8b3f7322f15c8cb_Response Length - dark mode](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/74l9X7ZzMmcKUlPRsLobS.png) ## 🏃 How to run - Hugging Face: [LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) - llama.cpp: [LFM2-350M-Math-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-Math-GGUF) - LEAP: [LEAP model library](https://leap.liquid.ai/models?model=lfm2-350M-math) ## 📬 Contact If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).