README.md

---
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｜>
```