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Model: quangdung/Qwen2.5-7B-Math-Distill-Sens Source: Original Platform
2026-07-07 20:44:11 +08:00
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+---
+license: apache-2.0
+
+language:
+  - en
+
+base_model:
+  - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+  - Qwen/Qwen2.5-Math-7B
+
+tags:
+  - merge
+  - sens-merging
+  - math
+  - reasoning
+  - qwen2.5
+  - deepseek-r1
+
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-Math-DeepSeekR1-Sens-7B
+
+A 7B merged model created by applying Sensitivity-aware Model Merging (Sens Merging) to:
+
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B  
+- Qwen/Qwen2.5-Math-7B  
+
+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.
+
+---
+
+## Highlights
+
+- Average accuracy: 66.9%  
+- Average output tokens: 701  
+- Output tokens reduced by 75.2% compared to DeepSeek-R1-Distill-Qwen-7B  
+- Only 2.5 points lower average accuracy than DeepSeek-R1-Distill-Qwen-7B  
+
+This model provides an attractive trade-off between reasoning quality and inference cost.
+
+---
+
+## Base Models
+
+| Model                        | Avg Accuracy | Avg Tokens |
+|-----------------------------|-------------:|-----------:|
+| DeepSeek-R1-Distill-Qwen-7B | 69.4         | 2826       |
+| Qwen2.5-Math-7B             | 45.3         | 755        |
+| Sens Merge (λ=0.4)          | 66.9         | 701        |
+
+---
+
+## Benchmark Results
+
+| Benchmark      | Distill | Qwen2.5-Math | Sens Merge (λ=0.4) |
+|----------------|--------:|-------------:|--------------------:|
+| College Math   | 66.0    | 37.9         | 70.4               |
+| GSM8K          | 90.2    | 84.5         | 90.6               |
+| MATH           | 94.4    | 73.3         | 90.2               |
+| Minerva Math   | 41.5    | 13.6         | 36.0               |
+| OlympiadBench  | 55.0    | 17.3         | 47.2               |
+| Avg Accuracy   | 69.4    | 45.3         | 66.9               |
+| Avg Tokens     | 2826    | 755          | 701                |
+
+---
+
+## Motivation
+
+Large reasoning models such as DeepSeek-R1-Distill often produce long chains of thought, which increases inference cost.
+
+This model explores whether model merging can reduce reasoning verbosity without requiring additional training.
+
+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:
+
+- Maintains competitive reasoning performance  
+- Produces significantly shorter outputs  
+- Requires no gradient-based fine-tuning  
+- Uses only a small calibration dataset  
+
+---
+
+## Comparison with DPO
+
+We additionally compared Sens Merging with a DPO-trained model:
+
+| Model                        | Avg Accuracy | Avg Tokens |
+|-----------------------------|-------------:|-----------:|
+| DeepSeek-R1-Distill-Qwen-7B | 69.4         | 2826       |
+| DPO                         | 68.55        | 2402       |
+| Sens Merge (λ=0.4)          | 66.9         | 701        |
+
+Sens Merging achieves a much larger reduction in output length while remaining competitive in accuracy.
+
+---
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "quangdung/Qwen2.5-Math-DeepSeekR1-Sens-7B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+prompt = "Solve: If x^2 + 5x + 6 = 0, find x."
+
+messages = [{"role": "user", "content": prompt}]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))