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--- |
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license: mit |
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train: false |
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inference: true |
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pipeline_tag: text-generation |
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base_model: |
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- mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1 |
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language: |
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- en |
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tags: |
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- DeepSeek-R1-Distill-Qwen2.5-1.5B |
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--- |
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Original Model : https://huggingface.co/mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1 |
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--- |
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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. |
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## Performance |
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| 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-v1.1">DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1</a> | |
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|:-------------------:|:--------:|:----------------:| |
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| ARC (25-shot) | 40.96 | <b>41.55</b> | |
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| HellaSwag (10-shot)| 44 | <b>45.88</b> | |
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| MMLU (5-shot) | 39.27 | <b>41.82</b> | |
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| TruthfulQA-MC2 | 45.17 | <b>46.63</b> | |
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| Winogrande (5-shot)| 55.49 | <b>57.7</b> | |
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| GSM8K (5-shot) | 69.9 | <b>74.3</b> | |
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| Average | 49.13 | <b>51.31</b> | |
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| 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-v1.1">DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1</a> | |
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|:-------------------:|:--------:|:----------------:| |
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| GPQA (0-shot) | 26.96 | <b>26.99</b> | |
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| MMLU PRO (5-shot) | 16.74 | <b>19.86</b> | |
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| MUSR (0-shot) | 35.93 | <b>36.6</b> | |
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| BBH (3-shot) | 35.12 | <b>37.23</b> | |
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| IfEval (0-shot) | 24.94 | <b>27.22</b> | |
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## Usage |
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```Python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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compute_dtype = torch.bfloat16 |
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device = 'cuda' |
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model_id = "mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1" |
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=compute_dtype, attn_implementation="sdpa", device_map=device) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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prompt = "What is 1.5+102.2?" |
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chat = tokenizer.apply_chat_template([{"role":"user", "content":prompt}], tokenize=True, add_generation_prompt=True, return_tensors="pt") |
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outputs = model.generate(chat.to(device), max_new_tokens=1024, do_sample=True) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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Output: |
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``` |
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<｜begin▁of▁sentence｜><｜User｜>What is 1.5+102.2?<｜Assistant｜><think> |
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First, I identify the numbers involved in the addition: 1.5 and 102.2. |
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Next, I add the whole numbers: 1 + 102 equals 103. |
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Then, I add the decimal parts: 0.5 + 0.2 equals 0.7. |
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Finally, I combine the results: 103 + 0.7 equals 103.7. |
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</think> |
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To solve the addition \(1.5 + 102.2\), follow these steps: |
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1. **Add the whole numbers:** |
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\[ |
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1 + 102 = 103 |
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\] |
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2. **Add the decimal parts:** |
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\[ |
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0.5 + 0.2 = 0.7 |
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\] |
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3. **Combine the results:** |
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\[ |
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103 + 0.7 = 103.7 |
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\] |
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So, the final answer is \(\boxed{103.7}\).<｜end▁of▁sentence｜> |
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``` |
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--- |
