--- license: mit train: false inference: true pipeline_tag: text-generation base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --- This is a version of the <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B">DeepSeek-R1-Distill-Qwen-1.5B</a> model re-distilled for better performance. ## Performance | Models | <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B">DeepSeek-R1-Distill-Qwen-1.5B</a> | <a href="https://huggingface.co/mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.0">DeepSeek-R1-ReDistill-Qwen-1.5B-v1.0</a> | |:-------------------:|:--------:|:----------------:| | ARC (25-shot) | 40.96 | <b>41.3</b> | | HellaSwag (10-shot)| 44 | <b>45.22</b> | | MMLU (5-shot) | 39.27 | <b>42.01</b> | | TruthfulQA-MC2 | 45.17 | <b>46.64</b> | | Winogrande (5-shot)| 55.49 | <b>56.75</b> | | GSM8K (5-shot) | 69.9 | <b>73.24</b> | | Average | 49.13 | <b>50.86</b> | | Models | <a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B">DeepSeek-R1-Distill-Qwen-1.5B</a> | <a href="https://huggingface.co/mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.0">DeepSeek-R1-ReDistill-Qwen-1.5B-v1.0</a> | |:-------------------:|:--------:|:----------------:| | GPQA (0-shot) | 26.96 | <b>27.8</b> | | MMLU PRO (5-shot) | 16.74 | <b>19.44</b> | | MUSR (0-shot) | 35.93 | <b>35.94</b> | | BBH (3-shot) | 35.12 | 35.11 | | IfEval (0-shot) | 24.94 | <b>27.1</b> | ## Usage ```Python import torch from transformers import AutoModelForCausalLM, AutoTokenizer compute_dtype = torch.bfloat16 device = 'cuda' model_id = "mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.0" model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=compute_dtype, attn_implementation="sdpa", device_map=device) tokenizer = AutoTokenizer.from_pretrained(model_id) prompt = "What is 1.5+102.2?" chat = tokenizer.apply_chat_template([{"role":"user", "content":prompt}], tokenize=True, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(chat.to(device), max_new_tokens=1024, do_sample=True) print(tokenizer.decode(outputs[0])) ``` Output: ``` <|begin▁of▁sentence|><|User|>What is 1.5+102.2?<|Assistant|><think> First, I identify the numbers involved in the addition: 1.5 and 102.2. Next, I add the whole numbers: 1 + 102 equals 103. Then, I add the decimal parts: 0.5 + 0.2 equals 0.7. Finally, I combine the results: 103 + 0.7 equals 103.7. </think> To solve the addition \(1.5 + 102.2\), follow these steps: 1. **Add the whole numbers:** \[ 1 + 102 = 103 \] 2. **Add the decimal parts:** \[ 0.5 + 0.2 = 0.7 \] 3. **Combine the results:** \[ 103 + 0.7 = 103.7 \] So, the final answer is \(\boxed{103.7}\).<|end▁of▁sentence|> ```