--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --- This is a quantization of the [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B). DeepSeek's Qwen-distilled models are compact reasoning models derived from DeepSeek-R1, achieving exceptional performance by distilling larger model reasoning patterns into smaller architectures. Spanning from 1.5B to 70B parameters, the models are based on Qwen2.5 and Llama3, with the standout DeepSeek-R1-Distill-Qwen-32B outperforming OpenAI-o1-mini and setting new dense model benchmarks. By combining reinforcement learning (RL) and supervised fine-tuning (SFT), these open-source models provide a powerful resource for advancing research and practical applications. ## Evaluations This model provides an accuracy recovery of 100.04%. | __English__ | __[DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)__ | __[DeepSeek-R1-Distill-Qwen-32B-FP8-Dynamic (this)](https://huggingface.co/cortecs/DeepSeek-R1-Distill-Qwen-32B-FP8-Dynamic)__ | |:--------------|------------------------------------------------------------------------------------------------------:|---------------------------------------------------------------------------------------------------------------------------------:| | Avg. | 74.03 | 74.06 | | ARC | 68.2 | 68.9 | | Hellaswag | 74 | 73.7 | | MMLU | 79.88 | 79.57 | We did not check for data contamination. Evaluation was done using [Eval. Harness](https://github.com/EleutherAI/lm-evaluation-harness) with `limit=1000`. ## Usage Install **vLLM** and run the [server](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#openai-compatible-server): ``` python -m vllm.entrypoints.openai.api_server --model cortecs/DeepSeek-R1-Distill-Qwen-32B-FP8-Dynamic ``` Access the model: ``` curl http://localhost:8000/v1/completions -H "Content-Type: application/json" -d ' { "model": "cortecs/DeepSeek-R1-Distill-Qwen-32B-FP8-Dynamic", "prompt": "San Francisco is a" } ' ```