This is a version of the DeepSeek-R1-Distill-Qwen-1.5B model re-distilled for better performance.
Performance
| Models | DeepSeek-R1-Distill-Qwen-1.5B | DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1 |
|---|---|---|
| ARC (25-shot) | 40.96 | 41.55 |
| HellaSwag (10-shot) | 44 | 45.88 |
| MMLU (5-shot) | 39.27 | 41.82 |
| TruthfulQA-MC2 | 45.17 | 46.63 |
| Winogrande (5-shot) | 55.49 | 57.7 |
| GSM8K (5-shot) | 69.9 | 74.3 |
| Average | 49.13 | 51.31 |
| Models | DeepSeek-R1-Distill-Qwen-1.5B | DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1 |
|---|---|---|
| GPQA (0-shot) | 26.96 | 26.99 |
| MMLU PRO (5-shot) | 16.74 | 19.86 |
| MUSR (0-shot) | 35.93 | 36.6 |
| BBH (3-shot) | 35.12 | 37.23 |
| IfEval (0-shot) | 24.94 | 27.22 |
Usage
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｜>