DeepSeek-Fine-Tuning with Unsloth & Alpaca-GPT4🔧

🧠 Overview

This repository demonstrates instruction fine-tuning of the DeepSeek Large Language Model using the vicgalle/alpaca-gpt4 dataset and Unsloth’s FastLanguageModel.from_pretrained for highly efficient training.

The project focuses on fast, memory-optimized LLM fine-tuning, enabling large-model experimentation on limited hardware while maintaining strong instruction-following performance.

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🚀 Key Highlights

⚡ Unsloth FastLanguageModel for optimized model loading

🧠 Instruction fine-tuning with alpaca-gpt4

🔧 Modular, extensible training pipeline

💾 Reduced VRAM usage and faster training

📦 Clean separation of configs, scripts, core logic, and tests

📘 Dataset

vicgalle/alpaca-gpt4

A high-quality instruction dataset generated using GPT-4, designed to align open-source LLMs with human-like instruction following.

Sample format:

{
  "instruction": "Explain bias vs variance.",
  "input": "",
  "output": "Bias refers to systematic error..."
}

This dataset improves:

Instruction adherence

Reasoning quality

Response clarity and structure

🧠 Model Loading (Unsloth)

The model is loaded using Unsloth’s optimized API, enabling:

Faster initialization

Memory-efficient training

Seamless PEFT / LoRA integration

from unsloth import FastLanguageModel

def load_deepseek():
    logger.info("🚀 Loading DeepSeek Model")
    return FastLanguageModel.from_pretrained( **model_config()['DeepSeek'] # or use a custom map if needed
    )

This approach allows fine-tuning large models on consumer-grade GPUs.

🏗️ Project Structure

DeepSeek-Fine-Tuning/
│
├── config/               # Training & model configs
├── scripts/              # Training and inference scripts
├── src/                  # Core fine-tuning logic
├── test/                 # Validation tests
├── run_pipeline.sh       # End-to-end pipeline execution
├── README.md
└── LICENSE

🛠️ Tech Stack

• Python

• PyTorch

• Hugging Face Transformers & Datasets

• Unsloth

• DeepSeek LLM

• CUDA

⚙️ Setup & Installation

git clone https://github.com/mohsinraza2999/DeepSeek-Fine-Tuning.git
cd DeepSeek-Fine-Tuning
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Requirements

Python 3.9+

NVIDIA GPU (CUDA recommended)

Minimum VRAM depends on sequence length and batch size

🚀 Training

Run the complete fine-tuning pipeline:

run_pipeline.sh

This performs:

Dataset loading (alpaca-gpt4)

Optimized model initialization (Unsloth)

Instruction fine-tuning

Checkpoint saving

🔍 Inference

python scripts/infer.py \ --prompt "Explain gradient descent in simple terms."

The fine-tuned model produces:

More structured answers

Better reasoning

Improved instruction compliance vs base model

📊 Evaluation

Current evaluation:

Qualitative comparison with base DeepSeek

Instruction-following accuracy

Coherence and relevance of responses

Planned improvements:

Automated benchmarks

Human preference evaluation

Task-specific metrics

📈 Results & Observations

• ⚡ Faster training compared to standard HF loading

• 💾 Significant VRAM reduction using Unsloth

• 📈 Improved instruction adherence after fine-tuning

• 🧠 Stable convergence on alpaca-gpt4

• 💡 Use Cases

Instruction-following AI assistants

Technical and educational Q&A systems

Foundation for RAG-based LLM applications

Cost-efficient enterprise LLM adaptation

🔮 Future Enhancements

LoRA hyperparameter experiments

RAG integration with vector databases

MLflow / W&B experiment tracking

FastAPI inference service

Quantized deployment (4-bit / 8-bit)

👨‍💻 Author

Mohsin Raza AI / ML Engineer 🔗 https://github.com/mohsinraza2999