Dzluck/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1-GGUF overview

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license: mit

train: false

inference: true

pipeline_tag: text-generation

base_model:

• mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1

language:

• en

tags:

• DeepSeek-R1-Distill-Qwen2.5-1.5B

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Original Model : https://huggingface.co/mobiuslabsgmbh/DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1

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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.

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

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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