---

base_model: unsloth/qwen2.5-14b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
datasets:
- qingy2024/QwQ-LongCoT-Verified-130K

---

[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)


# QuantFactory/QwQ-14B-Math-v0.2-GGUF
This is quantized version of [qingy2024/QwQ-14B-Math-v0.2](https://huggingface.co/qingy2024/QwQ-14B-Math-v0.2) created using llama.cpp

# Original Model Card


# Uploaded  model

- **Developed by:** qingy2024
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-14b-bnb-4bit

This model is a fine-tuned version of **Qwen 2.5-14B**, trained on QwQ 32B Preview's responses to questions from the **NuminaMathCoT** dataset.

**Note:** This model uses the standard ChatML template.

At 500 steps, the loss was plateauing so I decided to stop training to prevent excessive overfitting.

---

#### Training Details

- **Base Model**: Qwen 2.5-14B
- **Fine-Tuning Dataset**: Verified subset of **NuminaMathCoT** using Qwen 2.5 3B Instruct as a judge. (the `sharegpt-verified-cleaned` subset from my dataset).
- **QLoRA Configuration**:  
  - **Rank**: 32  
  - **Rank Stabilization**: Enabled
- **Optimization Settings**:  
  - Batch Size: 8  
  - Gradient Accumulation Steps: 2 (Effective Batch Size: 16)  
  - Warm-Up Steps: 5  
  - Weight Decay: 0.01  
- **Training Steps**: 500 steps
- **Hardware Information**: A100-80GB

---


[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)