---
license: mit
base_model: unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit
tags:
- code
- amd
- rocm
- hip
- cuda
- unsloth
- lablab-ai
- ghost-coder
---

# 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.

---
*Created by Talha*