AhmedAyman/k2-think-cuda-1505

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 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

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:

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

@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 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
• Fine-tuning framework: PEFT
• Training infrastructure: Transformers