prav-974/bugfixer-deepseek-6.7b

🐛 BugFixer-DeepSeek-6.7B

A Fine-Tuned Code Correction Model

🔍 Overview

BugFixer-DeepSeek-6.7B is a fine-tuned version of the base model DeepSeek-Coder-6.7B-Instruct designed to automatically detect and fix bugs in source code.

The model takes buggy code as input and outputs a corrected version without explanation, making it suitable for automation pipelines and IDE integrations.

🧠 Model Details

• —Base Model: deepseek-ai/deepseek-coder-6.7b-instruct
• —Model Type: Causal Language Model (Decoder-only Transformer)
• —Fine-tuning Method: LoRA (Low-Rank Adaptation)
• —Task: Code Refinement / Bug Fixing
• —Framework: Hugging Face Transformers
• —License: Same as base model (check DeepSeek license)

🎯 Intended Use

✅ Direct Use

• —Fix Python bugs automatically
• —Code debugging assistant
• —Integration in:

• —VS Code extensions
• —CI/CD pipelines
• —AI coding assistants

🔄 Downstream Use

• —Code auto-repair systems
• —Educational tools (learning debugging)
• —Static analysis augmentation

❌ Out-of-Scope Use

• —Not designed for:

• —Production-critical code validation
• —Security-sensitive systems
• —Non-code natural language tasks

📊 Training Details

📂 Dataset

• —Dataset: CodeXGLUE — Code Refinement
• —Contains:

• —Buggy code snippets
• —Corrected versions

⚙️ Training Procedure

• —Hardware: Kaggle (2× T4 GPUs)
• —Precision: FP16
• —Method: LoRA fine-tuning
• —Epochs: 1
• —Batch Size: 1 (with gradient accumulation)
• —Max Sequence Length: 512

🔧 Hyperparameters

🧪 Evaluation

📌 Metrics Used

• —Training Loss
• —Qualitative Code Correctness

📈 Observations

• —Strong performance on:

• —Syntax errors
• —Index errors
• —Simple logical bugs

• —Moderate performance on:

• —Complex algorithms
• —Multi-file dependencies

⚠️ Limitations

• —Primarily trained on Python → weaker on other languages
• —May:

• —Produce partially correct fixes
• —Miss deep logical bugs
• —No guarantee of optimal or efficient solutions

⚠️ Risks & Bias

• —Model may:

• —Introduce new bugs
• —Generate insecure code
• —Should always be reviewed by a developer

🚀 Usage

🔹 Load Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "<your-username>/bugfixer-deepseek-6.7b",
    torch_dtype="auto",
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(
    "<your-username>/bugfixer-deepseek-6.7b"
)

🔹 Example

prompt = """### Instruction:
Fix the bug in the following code.

### Buggy Code:
def is_even(n):
    return n % 2 = 0

### Fixed Code:
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🧩 Model Variants

🔹 Adapter Version

• —Repo: <your-username>/bugfixer-deepseek-6.7b-adapter
• —Requires base model

🔹 Full Model

• —Repo: <your-username>/bugfixer-deepseek-6.7b
• —Fully merged and ready to use

🌍 Environmental Impact

• —Hardware: NVIDIA T4 ×2
• —Platform: Kaggle
• —Estimated Duration: Few hours
• —Precision: FP16 (reduced energy vs FP32)

🏗️ Technical Architecture

• —Transformer-based decoder model
• —Self-attention layers
• —LoRA applied to:

• —Query (q_proj)
• —Key (k_proj)
• —Value (v_proj)
• —Output (o_proj)

📚 Citation

If you use this model, please cite:

@misc{bugfixer_deepseek_6_7b,
  title={BugFixer-DeepSeek-6.7B},
  author={<your-name>},
  year={2026},
  note={Fine-tuned on CodeXGLUE dataset using LoRA}
}

📬 Contact

• —Developer: praveen
• —Hugging Face: https://huggingface.co/prav-974

⭐ Acknowledgements

• —DeepSeek AI for base model
• —Hugging Face for ecosystem
• —CodeXGLUE dataset contributors