Model Overview
rohitnagareddy/Qwen3-0.6B-Coding-Finetuned-v1 is a specialized 0.8 billion parameter language model derived from Qwen/Qwen3-0.6B. It has been fine-tuned using QLoRA (Quantized Low-Rank Adaptation) to excel in Python code generation based on natural language instructions.
Key Capabilities
- Instruction-based Python Code Generation: Designed to understand and fulfill programming requests, generating Python code snippets.
- Efficiency: Optimized for performance through 4-bit quantization during training, making it suitable for various deployment scenarios.
- GGUF Support: Available in multiple quantized GGUF versions (FP16, Q8_0, Q5_K_M, Q4_K_M) for compatibility with
llama.cpp and similar tools, offering flexibility for different hardware and performance needs.
Important Considerations
- Code Verification: Generated code requires thorough testing and review for correctness and optimality.
- Security: Code is not vetted for vulnerabilities; caution is advised for security-sensitive applications.
- Developer Assistant: Intended as a tool to aid developers, not to replace human expertise.
Training Details
The model was trained for 1 epoch on the TokenBender/code_instructions_122k_alpaca_style dataset, utilizing a QLoRA rank of 16, QLoRA alpha of 32, and a learning rate of 2e-4 with a Paged AdamW 32-bit optimizer.