varuneshv/VCoder

TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 10, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

VCoder is a 3.1 billion parameter Python-focused coding assistant fine-tuned from Qwen2.5-Coder-3B-Instruct by varuneshv. Optimized using LoRA and Unsloth on 15,000 Python instruction-response examples, it excels at Python code generation, problem solving, debugging, and algorithm implementation. The model demonstrates a 7.0% Pass@1 improvement over its base model on HumanEval coding tasks, making it suitable for various Python development needs. It supports a 32768-token context length and is compatible with GGUF-supporting platforms like Ollama and LM Studio.

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VCoder: A Python-Focused Code Generation Model

VCoder is a 3.1 billion parameter language model developed by varuneshv, specifically fine-tuned for Python programming tasks. It is based on the Qwen2.5-Coder-3B-Instruct architecture and was optimized using LoRA (Low-Rank Adaptation) with the Unsloth framework.

Key Capabilities

  • Enhanced Python Code Generation: Fine-tuned on the "Python Code Instructions 15K" dataset, comprising 15,000 instruction-response examples, VCoder is highly proficient in generating Python code.
  • Problem Solving and Debugging: Excels at algorithm design, data structures, debugging, code refactoring, and solving coding interview questions.
  • Performance Improvement: Achieves a 7.0% Pass@1 improvement over its base model (Qwen2.5-Coder-3B-Instruct) on HumanEval coding tasks, demonstrating superior performance in Python coding scenarios.
  • Efficient Fine-Tuning: Utilizes parameter-efficient fine-tuning (LoRA) for effective adaptation while maintaining computational efficiency.
  • GGUF Compatibility: Available in GGUF Q8_0 quantized format, making it compatible with platforms like Ollama, LM Studio, and llama.cpp.

Good For

  • Developers requiring a specialized assistant for Python code generation.
  • Tasks involving algorithm implementation and data structure manipulation in Python.
  • Debugging and refactoring existing Python codebases.
  • Practicing for coding interviews or competitive programming in Python.

Limitations

While highly effective for Python, VCoder is primarily optimized for this language. Its performance on highly specialized domains or non-Python tasks may be limited, and it should not be the sole source for production-critical code.