---
license: apache-2.0
tags:
- math
- reasoning
- qwen
- llama-cpp
- gguf
- lora
- chain-of-thought
datasets:
- nvidia/Nemotron-SFT-Math-v3
base_model:
- Qwen/Qwen3.5-4B
---

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

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.