metadata
license: mit
train: false
inference: false
pipeline_tag: text-generation
This is a version of the DeepSeek-R1-Distill-Qwen-7B model re-distilled for better performance.
Performance
| Models | DeepSeek-R1-Distill-Qwen-7B | DeepSeek-R1-ReDistill-Qwen-7B-v1.1 |
|---|---|---|
| ARC (25-shot) | 55.03 | 52.3 |
| HellaSwag (10-shot) | 61.9 | 62.36 |
| MMLU (5-shot) | 56.75 | 59.53 |
| TruthfulQA-MC2 | 45.76 | 47.7 |
| Winogrande (5-shot) | 60.38 | 61.8 |
| GSM8K (5-shot) | 78.85 | 83.4 |
| Average | 59.78 | 61.18 |
| Models | DeepSeek-R1-Distill-Qwen-7B | DeepSeek-R1-ReDistill-Qwen-7B-v1.1 |
|---|---|---|
| GPQA (0-shot) | 30.9 | 34.99 |
| MMLU PRO (5-shot) | 28.83 | 31.02 |
| MUSR (0-shot) | 38.85 | 44.42 |
| BBH (3-shot) | 43.54 | 51.53 |
| IfEval (0-shot) - strict | 42.33 | 35.49 |
| IfEval (0-shot) - loose | 30.31 | 38.49 |
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
compute_dtype = torch.bfloat16
device = 'cuda'
model_id = "mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-7B-v1.1"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=compute_dtype, attn_implementation="sdpa", device_map=device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
chat = tokenizer.apply_chat_template([{"role":"user", "content":"What is 1.5+102.2?"}], 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｜>