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varuneshv/VCoder

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face
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Model Card

VCoder

VCoder is a Python-focused coding assistant fine-tuned from Qwen2.5-Coder-3B-Instruct using LoRA and Unsloth.

The model was trained on 15,000 Python instruction-response examples from the Python Code Instructions 15K dataset and optimized for Python code generation, problem solving, debugging, and algorithm implementation.

Model Details

AttributeValue
Base ModelQwen2.5-Coder-3B-Instruct
Fine-Tuning MethodLoRA
FrameworkUnsloth
DatasetPython Code Instructions 15K
Training Samples15,000
GPUNVIDIA Tesla T4
Quantized FormatGGUF Q8_0
Primary LanguagePython

Training Pipeline

Training was performed incrementally:

StageSamples
Stage 10 - 5,000
Stage 25,000 - 10,000
Stage 310,000 - 15,000

The model was trained using parameter-efficient fine-tuning (LoRA), allowing adaptation of the base model while keeping computational requirements low.


Benchmark Results

Output

HumanEval Comparison

The model was evaluated against the original Qwen2.5-Coder-3B-Instruct on HumanEval coding tasks.

ModelPass@1
Base Qwen2.5-Coder-3B61.0%
VCoder68.0%

Improvement

text
+7.0% Pass@1 improvement

This demonstrates that the fine-tuned model performs better on Python coding tasks than the original base model.


Example Usage

Python

python
prompt = """
### Instruction:
Write a Python function to reverse a string.

### Input:

### Response:
"""

Example Output

python
def reverse_string(text):
    return text[::-1]

Supported Tasks

  • Python Code Generation
  • Algorithm Design
  • Data Structures
  • Debugging
  • Code Refactoring
  • Coding Interview Questions
  • Competitive Programming
  • Function Completion

GGUF Usage

Compatible with:

  • Ollama
  • LM Studio
  • llama.cpp

Training Dataset

Dataset used:

Python Code Instructions 15K

The dataset contains instruction-response pairs focused on Python programming tasks including:

  • Function generation
  • Data manipulation
  • Algorithms
  • Debugging
  • Problem solving

Limitations

  • Primarily optimized for Python.
  • Benchmark performed on a subset of HumanEval tasks.
  • May generate incorrect code for highly specialized domains.
  • Should not be used as the sole source of production-critical code.

Acknowledgements

  • Qwen Team for Qwen2.5-Coder
  • Unsloth for efficient fine-tuning
  • Hugging Face
  • OpenAI HumanEval Benchmark

Citation

bibtex
@misc{vcoder2026,
  title={VCoder: Python Code Generation Model},
  author={Varunesh V, Prawin R K, Sarguru N},
  year={2026},
  base_model={Qwen2.5-Coder-3B-Instruct}
}

Github : https://github.com/varunesh-v Mail : varunesh.wrk@gmail.com