Ghost-Coder-Qwen2.5-32B-LoRA

Ghost-Coder: Qwen2.5-32B CUDA-to-HIP Translator

Ghost-Coder is a specialized LLM designed to bridge the gap between NVIDIA's proprietary CUDA and AMD's open ROCm ecosystem. This model is a fine-tuned version of Qwen2.5-Coder-32B-Instruct, optimized specifically for high-fidelity translation of GPU kernels.

Developed for the Lablab.ai AMD Developer Hackathon (2026).

🚀 Model Highlights

• Specialization: Maps complex CUDA logic (memory management, warp primitives, kernels) to functional AMD HIP code.
• Hardware-Aware: Fine-tuned specifically for execution on AMD Instinct hardware.
• Agent-Ready: Designed to be the "brain" of an autonomous, self-healing compiler loop.

🛠️ Training Details

The model was fine-tuned using the Unsloth framework on a high-speed sprint configuration to maximize generalization.

• Hardware: AMD Instinct MI300X (192GB VRAM)
• Base Model: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)
• Dataset: Curated subset of CASS (CUDA-to-HIP mapping pairs)
• Context Length: 4096
• Training Steps: 200
• Global Batch Size: 64

🧠 Intended Use

Ghost-Coder is intended for use in the Ghost-Harness, an agentic workflow that:

1. Translates CUDA source code to HIP.
2. Attempts compilation via hipcc.
3. Self-corrects based on compiler error feedback.

📝 Acknowledgements

Special thanks to AMD and Lablab.ai for providing the compute resources and the platform to build across the AI stack.

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Created by Talha