This model is a fine-tuned version of Qwen3.5-4B, explicitly optimized for complex mathematical reasoning and Chain-of-Thought (CoT) problem solving. It was fine-tuned using the Nemotron-Math-v3 dataset with Parameter-Efficient Fine-Tuning (PEFT/LoRA).

Model Details

Base Model: Qwen/Qwen3.5-4B
Fine-Tuning Dataset: nvidia/Nemotron-SFT-Math-v3
Methodology: LoRA (Rank = 64, Alpha = 32 or Alpha = 16). The lora_alpha scaling is specifically tuned to prevent catastrophic forgetting, ensuring the model retains conversational abilities while significantly enhancing mathematical logic.
Quantization: Safetensor format (F16) and GGUF formats (Q8_0)

Recommended Generation Parameters

Because this model leverages extensive Chain-of-Thought reasoning to solve math problems, the following generation parameters are highly recommended for the best performance:

json

{
  "temperature": 1.0,
  "top_p": 0.95,
  "repetition_penalty": 1.1
}

Note: A repetition_penalty of 1.1 is crucial to prevent the base model from occasionally falling into infinite generation loops on extremely long context windows.

Use Cases

Resolving complex math word problems (GSM8K).
Higher-level mathematical reasoning (MATH, AIME).
Step-by-step logic tracking and proofs.

DavidOKB/MathThink-Qwen-3.5-4B

Qwen3.5-4B Math Fine-Tuned (Nemotron-SFT-Math-v3)

Model Details

Recommended Generation Parameters

Use Cases

DavidOKB/
MathThink-Qwen-3.5-4B