rish3on3AI/chem-Phi-Mini

ChemPhi-Mini

ChemPhi-Mini is a lightweight chemistry-focused reasoning model fine-tuned from unsloth/phi-4-mini-reasoning-unsloth-bnb-4bit using supervised fine-tuning (SFT).

This project explores efficient domain adaptation for educational AI systems under constrained hardware environments. The goal was to build a compact chemistry tutoring and reasoning assistant capable of running locally with minimal GPU resources while maintaining useful scientific explanation capabilities.

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Project Goals

This project was built to explore:

• Parameter-efficient fine-tuning (PEFT)
• Low-resource LLM training workflows
• Chemistry-focused educational reasoning
• Lightweight local AI systems
• Quantized inference and deployment
• Linux-based AI experimentation

The model is part of a broader self-hosted AI and systems engineering learning workflow involving:

• Linux infrastructure
• Local inference pipelines
• GPU-constrained experimentation
• Open-source AI tooling

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Base Model

Base model used:

unsloth/phi-4-mini-reasoning-unsloth-bnb-4bit

Core characteristics:

• Phi-4 Mini Reasoning architecture
• 4-bit quantized
• Optimized using the Unsloth ecosystem
• Designed for efficient fine-tuning and inference

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Training Method

This model was fine-tuned using:

• LoRA (Low-Rank Adaptation)
• PEFT
• TRL SFTTrainer
• 4-bit quantization
• Supervised Fine-Tuning (SFT)

Training focused on:

• Chemistry explanations
• Conceptual reasoning
• Educational QA
• Scientific response formatting

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Hardware & Environment

Training environment:

• Google Colab
• NVIDIA T4 GPU
• CUDA-enabled PyTorch stack

This project specifically explored practical AI development under limited VRAM conditions.

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Tech Stack

• Transformers
• TRL
• PEFT
• Unsloth
• PyTorch
• Hugging Face ecosystem

Framework versions:

• PEFT 0.19.1
• TRL 0.24.0
• Transformers 5.5.0
• PyTorch 2.10.0+cu128
• Datasets 4.3.0
• Tokenizers 0.22.2

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Example Usage

from transformers import pipeline

generator = pipeline(
    "text-generation",
    model="rish3on3AI/ChemPhi-Mini",
    device="cuda"
)

messages = [
    {
        "role": "user",
        "content": "Explain why increasing temperature favors endothermic reactions."
    }
]

output = generator(
    messages,
    max_new_tokens=256,
    return_full_text=False
)

print(output[0]["generated_text"])