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Model: quangdung/Qwen2.5-7B-Math-Distill-Sens Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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- Qwen/Qwen2.5-Math-7B
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tags:
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- merge
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- sens-merging
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- math
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- reasoning
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- qwen2.5
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- deepseek-r1
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pipeline_tag: text-generation
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---
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# Qwen2.5-Math-DeepSeekR1-Sens-7B
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A 7B merged model created by applying Sensitivity-aware Model Merging (Sens Merging) to:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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- Qwen/Qwen2.5-Math-7B
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The goal of this model is to preserve the strong mathematical reasoning ability of DeepSeek-R1-Distill while significantly reducing reasoning verbosity and output token length.
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---
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## Highlights
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- Average accuracy: 66.9%
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- Average output tokens: 701
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- Output tokens reduced by 75.2% compared to DeepSeek-R1-Distill-Qwen-7B
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- Only 2.5 points lower average accuracy than DeepSeek-R1-Distill-Qwen-7B
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This model provides an attractive trade-off between reasoning quality and inference cost.
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---
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## Base Models
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| Model | Avg Accuracy | Avg Tokens |
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|-----------------------------|-------------:|-----------:|
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| DeepSeek-R1-Distill-Qwen-7B | 69.4 | 2826 |
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| Qwen2.5-Math-7B | 45.3 | 755 |
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| Sens Merge (λ=0.4) | 66.9 | 701 |
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---
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## Benchmark Results
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| Benchmark | Distill | Qwen2.5-Math | Sens Merge (λ=0.4) |
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|----------------|--------:|-------------:|--------------------:|
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| College Math | 66.0 | 37.9 | 70.4 |
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| GSM8K | 90.2 | 84.5 | 90.6 |
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| MATH | 94.4 | 73.3 | 90.2 |
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| Minerva Math | 41.5 | 13.6 | 36.0 |
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| OlympiadBench | 55.0 | 17.3 | 47.2 |
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| Avg Accuracy | 69.4 | 45.3 | 66.9 |
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| Avg Tokens | 2826 | 755 | 701 |
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---
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## Motivation
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Large reasoning models such as DeepSeek-R1-Distill often produce long chains of thought, which increases inference cost.
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This model explores whether model merging can reduce reasoning verbosity without requiring additional training.
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By merging a reasoning model (DeepSeek-R1-Distill-Qwen-7B) with a compact mathematical model (Qwen2.5-Math-7B) using Sensitivity-aware Model Merging, the merged model:
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- Maintains competitive reasoning performance
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- Produces significantly shorter outputs
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- Requires no gradient-based fine-tuning
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- Uses only a small calibration dataset
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---
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## Comparison with DPO
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We additionally compared Sens Merging with a DPO-trained model:
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| Model | Avg Accuracy | Avg Tokens |
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|-----------------------------|-------------:|-----------:|
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| DeepSeek-R1-Distill-Qwen-7B | 69.4 | 2826 |
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| DPO | 68.55 | 2402 |
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| Sens Merge (λ=0.4) | 66.9 | 701 |
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Sens Merging achieves a much larger reduction in output length while remaining competitive in accuracy.
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---
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "quangdung/Qwen2.5-Math-DeepSeekR1-Sens-7B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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prompt = "Solve: If x^2 + 5x + 6 = 0, find x."
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messages = [{"role": "user", "content": prompt}]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=512
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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54
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'Please reason step by step, and put your final answer within \\boxed{}.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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62
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 4096,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 10000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "5.5.4",
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"use_cache": true,
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"use_mrope": false,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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7
generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": false,
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"eos_token_id": 151643,
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"max_new_tokens": 2048,
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"transformers_version": "5.5.4"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e9c8685245e89d40805ff6eda9ba04fb0269d153d529961d712e41b4bad86ffa
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size 15231272152
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3
tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": true,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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