alex2110/qwen2.5-0.5b-code-bcp-v2

🚀 Qwen2.5-0.5B-Code-BCP-V2

📝 Overview

This model is a fine-tuned version of Qwen2.5-0.5B-Instruct, specialized for real-time code refactoring, logging injection, and algorithmic optimization. It is designed to power VSCode extensions where low latency and local execution are critical.

Compared to the base model, BCP-V2 demonstrates an emergent understanding of time complexity (O(n) awareness) and strictly follows developer-centric instructions without unnecessary conversational filler ("Zero-Yapping").

Key Capabilities:

• —Optimization: Identifying and refactoring nested loops into Hash Map lookups.
• —Structured Logging: Injecting custom-formatted logs (e.g., [MONITOR] templates).
• —Logic Transformation: Converting recursive functions to iterative patterns.
• —IDE Ready: Optimized for GGUF format for seamless integration with Ollama or llama.cpp.

📊 Training Details

• —Base Model: Qwen2.5-0.5B-Instruct (4-bit quantized)
• —Framework: Unsloth
• —Dataset: iamtarun/python_code_instructions_18k_alpaca
• —Method: LoRA (Low-Rank Adaptation)
• —Steps: 600 steps (~4,800 examples processed)
• —Batch Size: 8 (2 per device × 4 accumulation steps)
• —Scheduler: Cosine learning rate decay
• —Optimizer: AdamW 8-bit

📈 Evaluation: V1 vs. V2 Comparison

During development, we analyzed the impact of training duration on algorithmic reasoning.

Notable Improvements in V2:

• —Test Case (Hash Map): While V1 failed to optimize nested loops, V2 correctly identified the need for a lookup dictionary to improve performance from $O(n^2)$ to $O(n)$.
• —Test Case (Logging): V2 handles complex string interpolation (e.g., using **locals()) while maintaining strict template formatting.

💻 Usage

Prompt Format:

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{code_snippet}

### Response:
{code_snipped_refactored}

Running with Ollama:

1. 1.Download the .gguf file from this repository.

1. 1.Create a Modelfile: FROM ./qwen2.5-0.5b-instruct.Q4KM.gguf TEMPLATE "{{ .Prompt }}"

1. 1.Run: ollama create bcp-v2 -f Modelfile

⚠️ Limitations

As a 0.5B parameter model, BCP-V2 is highly efficient but may occasionally produce minor syntax errors in very complex logic. It is best used for refactoring snippets of up to 50 lines and as a high-speed coding assistant.

🤝 Collaboration

This model was developed as part of a project to create an intelligent local-first VSCode extension chatbot.

Lead Fine-tuning Engineer: Alex (alex2110)