---
license: apache-2.0
base_model: LLM360/K2-Think
tags:
- cuda
- gpu
- programming
- kernel
- optimization
- rocm
- peft
- lora
library_name: peft
pipeline_tag: text-generation
---

# K2-Think CUDA Model

A specialized Large Language Model fine-tuned for CUDA/ROCm kernel development and optimization tasks.

## Model Description

This model is based on [LLM360/K2-Think](https://huggingface.co/LLM360/K2-Think) and has been fine-tuned using LoRA (Low-Rank Adaptation) to specialize in CUDA programming tasks. The model can help developers with GPU programming, kernel optimization, and CUDA best practices.

### Model Architecture

- **Base Model**: LLM360/K2-Think
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Target Modules**: gate_proj, v_proj, o_proj, k_proj, up_proj, down_proj, q_proj
- **LoRA Rank**: 16
- **LoRA Alpha**: 32
- **LoRA Dropout**: 0.05

## Intended Use

This model is designed to assist developers with:

- Writing CUDA kernels
- Memory optimization strategies
- Performance tuning
- CUDA best practices
- ROCm development
- GPU programming concepts
- Kernel debugging and optimization

## Training Data

The model was fine-tuned on CUDA programming datasets, including:
- CUDA kernel implementations
- Memory optimization examples
- Performance tuning guides
- CUDA best practices documentation

## Training Procedure

### Training Hyperparameters

- **Learning Rate**: Variable (adaptive)
- **Batch Size**: Optimized for available hardware
- **Training Steps**: Multiple checkpoints (1000, 1505 steps)
- **Optimizer**: AdamW
- **Quantization**: 4-bit (when applicable)

### Training Infrastructure

- **Hardware**: GPU-accelerated training
- **Framework**: PyTorch with Transformers
- **Fine-tuning Library**: PEFT (Parameter Efficient Fine-Tuning)

## Performance

The model has been evaluated on CUDA programming tasks and shows improved performance in:
- CUDA kernel generation
- Memory optimization suggestions
- Performance analysis
- Code quality and best practices

## Usage

### Basic Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("LLM360/K2-Think")
tokenizer = AutoTokenizer.from_pretrained("LLM360/K2-Think")

# Load fine-tuned model
model = PeftModel.from_pretrained(base_model, "AhmedAyman/k2-think-cuda-1505")

# Generate response
prompt = "Write a CUDA kernel for matrix multiplication"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

### Using with Gradio

A Gradio interface is available for interactive use:

```python
import gradio as gr
from gradio_app import create_interface

demo = create_interface()
demo.launch()
```

## Available Checkpoints

- **1505**: Latest fine-tuned checkpoint (recommended)
- **1000**: Earlier checkpoint
- **base**: Original K2-Think model

## Limitations

- The model is specialized for CUDA/ROCm programming and may not perform as well on general programming tasks
- Performance depends on the quality and specificity of the input prompts
- The model may generate code that needs validation and testing

## Bias and Safety

This model inherits biases from its base model and training data. Users should:
- Validate generated code before use
- Test CUDA kernels in safe environments
- Follow CUDA programming best practices
- Be aware of potential security implications

## Environmental Impact

The model uses efficient fine-tuning techniques (LoRA) to minimize computational requirements. When using quantization, the model can run on consumer hardware with reduced memory requirements.

## Citation

```bibtex
@misc{k2-think-cuda,
  title={K2-Think CUDA Model: Specialized LLM for CUDA/ROCm Kernel Development},
  author={Ahmed Ayman},
  year={2025},
  url={https://huggingface.co/AhmedAyman/k2-think-cuda}
}
```

## License

This model is released under the Apache 2.0 License. See the [LICENSE](LICENSE) file for more details.

## Contact

For questions, issues, or contributions, please open an issue on the model repository or contact the maintainers.

## Acknowledgments

- Base model: [LLM360/K2-Think](https://huggingface.co/LLM360/K2-Think)
- Fine-tuning framework: [PEFT](https://github.com/huggingface/peft)
- Training infrastructure: [Transformers](https://github.com/huggingface/transformers)