You can deploy this model and start earning money today!

banty1614/
codeforge-qwen-lora

Name: codeforge-qwen-lora
Availability: InStock

Views

No views yet

🚀 CodeForge AI

Fine-tuned Qwen2.5-3B-Instruct using QLoRA + PEFT for software engineering tasks.

🚀 CodeForge AI

A QLoRA fine-tuned version of Qwen2.5-3B-Instruct for software engineering and Python coding tasks.

📖 Overview

CodeForge AI is a parameter-efficient fine-tuned (PEFT) Large Language Model built on Qwen2.5-3B-Instruct.

The objective of this project is to demonstrate a complete end-to-end modern LLM fine-tuning workflow using:

QLoRA
LoRA
PEFT
Hugging Face Transformers
TRL SFTTrainer
Google Colab Tesla T4

Instead of updating all 3.1 billion parameters, only 0.96% of the parameters were trained, making the project lightweight and GPU-efficient.

🏗 Base Model

Model

Qwen/Qwen2.5-3B-Instruct

⚡ Fine-Tuning Technique

QLoRA (4-bit Quantization)
LoRA (Low-Rank Adaptation)
PEFT (Parameter Efficient Fine Tuning)
Supervised Fine-Tuning (SFT)

📚 Dataset

Dataset used:

iamtarun/python_code_instructions_18k_alpaca

Dataset contains approximately 18,000 Python instruction-following examples.

For this proof-of-concept training run, a subset of the dataset was used.

⚙ Training Configuration

Parameter	Value
Base Model	Qwen2.5-3B-Instruct
Fine-Tuning	QLoRA
LoRA Rank (r)	16
Alpha	32
Dropout	0.05
Quantization	4-bit NF4
Optimizer	AdamW
Learning Rate	2e-4
Max Steps	100
Batch Size	2
Gradient Accumulation	4
Max Sequence Length	512
GPU	Tesla T4 (16 GB)

📊 Training Results

Training completed successfully.

Final Training Loss:

0.7047

Trainable Parameters:

29,933,568

Total Parameters:

3,115,872,256

Percentage of Trainable Parameters:

0.9607%

🧠 Architecture

Python Instruction Dataset
            │
            ▼
      Tokenization
            │
            ▼
Qwen2.5-3B-Instruct
            │
            ▼
    4-bit Quantization
            │
            ▼
          QLoRA
            │
            ▼
      LoRA Adapters
            │
            ▼
     Supervised Fine-Tuning
            │
            ▼
     Fine-Tuned Adapter
            │
            ▼
      Hugging Face Hub

💻 Usage

python

from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained(
    "banty1614/codeforge-qwen-lora",
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(
    "banty1614/codeforge-qwen-lora"
)

Example:

python

messages = [
    {
        "role": "user",
        "content": "Write a Python function to reverse a linked list."
    }
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

📈 Evaluation

The model was compared with the original base model on multiple Python programming prompts.

Evaluation included:

Code correctness
Explanation quality
Readability
Response formatting

This project demonstrates the complete QLoRA fine-tuning workflow rather than claiming to outperform the original Qwen model across all programming tasks.

⚠ Limitations

Trained for only 100 optimization steps.
Uses a subset of the original dataset.
Intended as a proof-of-concept for parameter-efficient fine-tuning.
Not evaluated on standardized coding benchmarks.

🚀 Future Work

Train on the complete dataset.
Increase training epochs.
Add evaluation on HumanEval and MBPP.
Expand training to FastAPI, SQL, Docker, Git, and System Design.
Support multiple LoRA adapters for different software engineering domains.

🙏 Acknowledgements

Alibaba Cloud for Qwen2.5
Hugging Face
TRL
PEFT
BitsAndBytes
Google Colab

If you use this project for learning or research, please consider giving the repository a ⭐.