---
base_model: unsloth/qwq-32b-preview-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---

# QWQ-32B Model Card

- **Developed by:** Daemontatox  
- **License:** Apache-2.0  
- **Base Model:** [unsloth/qwq-32b-preview-bnb-4bit](https://huggingface.co/unsloth/qwq-32b-preview-bnb-4bit)  

## Model Overview
The QWQ-32B model is an advanced large language model (LLM) designed for high-performance text generation tasks. It has been finetuned from the base model using the [Unsloth](https://github.com/unslothai/unsloth) framework and Hugging Face's TRL library, achieving superior speed and efficiency during training. 

### Key Features
- **Enhanced Training Speed:** Training was completed 2x faster compared to traditional methods, thanks to the optimization techniques provided by Unsloth.
- **Transformer-Based Architecture:** Built on the Qwen2 architecture, ensuring state-of-the-art performance in text generation and comprehension.
- **Low-Bit Quantization:** Utilizes 4-bit quantization (bnb-4bit), offering a balance between performance and computational efficiency.

### Use Cases
- Creative Writing and Content Generation
- Summarization and Translation
- Dialogue and Conversational Agents
- Research Assistance

### Performance Metrics
The QWQ-32B model demonstrates SOTA-level benchmarks across multiple text-generation datasets, highlighting its capabilities in both reasoning and creativity-focused tasks. Detailed evaluation results will be released in an upcoming report.

### Model Training
The finetuning process leveraged:
- [Unsloth](https://github.com/unslothai/unsloth): A next-generation framework for faster and efficient LLM training.
- Hugging Face's [TRL library](https://huggingface.co/docs/trl): Tools for reinforcement learning with human feedback (RLHF).

### Limitations
- Requires significant GPU resources for deployment despite the 4-bit quantization.
- Not explicitly designed for domain-specific tasks; additional fine-tuning may be required.

### Getting Started
You can load the model with Hugging Face's Transformers library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Daemontatox/QWQ-32B")
model = AutoModelForCausalLM.from_pretrained("Daemontatox/QWQ-32B", device_map="auto", load_in_4bit=True)

inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Acknowledgments
Special thanks to the Unsloth team and the Hugging Face community for their support and tools, making the development of QWQ-32B possible.

[![Made with Unsloth](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)](https://github.com/unslothai/unsloth)