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+deepseek-r1-redistill-qwen-1.5b-v1.1-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+deepseek-r1-redistill-qwen-1.5b-v1.1-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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+imatrix.dat filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+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-v1.1">DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1</a> | 
+|:-------------------:|:--------:|:----------------:|
+| ARC (25-shot)      | 40.96 | <b>41.55</b>  | 
+| HellaSwag (10-shot)| 44    | <b>45.88</b> |
+| MMLU (5-shot)      | 39.27 | <b>41.82</b> | 
+| TruthfulQA-MC2     | 45.17 | <b>46.63</b> | 
+| Winogrande (5-shot)| 55.49 | <b>57.7</b> | 
+| GSM8K (5-shot)     | 69.9  | <b>74.3</b> | 
+| Average            | 49.13 | <b>51.31</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-v1.1">DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1</a>  | 
+|:-------------------:|:--------:|:----------------:|
+| GPQA (0-shot)     | 26.96 | <b>26.99</b>  | 
+| MMLU PRO (5-shot) | 16.74 | <b>19.86</b> | 
+| MUSR (0-shot)     | 35.93 | <b>36.6</b> | 
+| BBH (3-shot)      | 35.12 | <b>37.23</b> | 
+| IfEval (0-shot)   | 24.94 | <b>27.22</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-v1.1"
+
+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｜>
+```

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