Update README.md

README.md

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 ---
 license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-3B-Instruct
+pipeline_tag: text-generation
+tags:
+- code-generation
+- python
+- qwen
+- unsloth
+- gguf
+- coding-assistant
+language:
+- en
 ---
+
+# 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
+
+| Attribute | Value |
+|------------|---------|
+| Base Model | Qwen2.5-Coder-3B-Instruct |
+| Fine-Tuning Method | LoRA |
+| Framework | Unsloth |
+| Dataset | Python Code Instructions 15K |
+| Training Samples | 15,000 |
+| GPU | NVIDIA Tesla T4 |
+| Quantized Format | GGUF Q8_0 |
+| Primary Language | Python |
+
+---
+
+## Training Pipeline
+
+Training was performed incrementally:
+
+| Stage | Samples |
+|---------|---------|
+| Stage 1 | 0 - 5,000 |
+| Stage 2 | 5,000 - 10,000 |
+| Stage 3 | 10,000 - 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
+
+### HumanEval Comparison
+
+The model was evaluated against the original Qwen2.5-Coder-3B-Instruct on HumanEval coding tasks.
+
+| Model | Pass@1 |
+|---------|---------|
+| Base Qwen2.5-Coder-3B | 61.0% |
+| VCoder | 68.0% |
+
+### 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
+
+### Ollama
+
+```text
+FROM ./VCoder.Q8_0.gguf
+```
+
+Build:
+
+```bash
+ollama create vcoder -f Modelfile
+```
+
+Run:
+
+```bash
+ollama run vcoder
+```
+
+---
+
+## 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