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.
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
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
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
Hardware & Environment
Training environment:
- Google Colab
- NVIDIA T4 GPU
- CUDA-enabled PyTorch stack
This project specifically explored practical AI development under limited VRAM conditions.
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
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"])
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