---
base_model: unsloth/phi-4-mini-reasoning-unsloth-bnb-4bit
library_name: peft
model_name: ChemPhi-Mini
license: apache-2.0
pipeline_tag: text-generation
tags:
- chemistry
- educational-ai
- lora
- qlora
- sft
- transformers
- trl
- unsloth
- reasoning
- peft
- local-llm
---

# 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

```python
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"])