akshatladdha16/Llama-3.2-3B-Chemistry-Tutor-LoRA

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This is a PEFT (Parameter Efficient Fine-Tuning) adapter trained on chemistry educational content using QLoRA (Quantized Low-Rank Adaptation) technique. The adapter is designed to enhance Llama-3.2-3B's capabilities in answering chemistry-related questions.

Model Details

• Base Model: meta-llama/Llama-3.2-3B
• Training Technique: QLoRA (4-bit quantization)
• Domain: Chemistry Education
• Language: English
• License: Same as base model

Model Description

This model is a QLoRA fine-tuned version of Meta-Llama-3.2-3B specifically optimized for chemistry question-answering tasks. The adapter layers were trained on a diverse chemistry dataset containing 4.4k+ educational Q&A pairs covering fundamental to advanced chemistry concepts.

Use Cases

• Answering chemistry concepts and definitions
• Explaining chemical processes and reactions
• Solving basic chemistry problems
• Providing chemistry educational content

Example Usage

Created a dedicated Google Collab notebook for anyone to infer the fine tuned adapter layer with base model. Use your personal access token from huggingface account Google colab Notebook: [https://colab.research.google.com/drive/16N_lnLKieJjMunvIXb59LtGavifx96nx#scrollTo=nd3kQhZbm2z9]

Training Setup

• Training Type: QLoRA fine-tuning
• Hardware: 4GB VRAM GPU optimization
• Quantization: 4-bit (NF4 format)
• LoRA Configuration:
  • Rank: 16
  • Alpha: 32
  • Target Modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
  • Dropout: 0.05

Training Data

The model was fine-tuned on a curated dataset of chemistry educational content, focusing on:

• Basic chemistry concepts
• Chemical processes and reactions
• Problem-solving examples
• NCERT chemistry curriculum

[https://huggingface.co/datasets/KadamParth/NCERT_Chemistry_12th]

Training Setup

Preprocessing

• Data Format: Chat-format JSONL with messages/roles structure
• Tokenization:
  • Max Length: 512 tokens
  • Padding: Right-side padding with EOS token
  • Special Tokens: Added conversation markers (User:, Assistant:)
• Prompt Template:

  ### Conversation:
  User: {chemistry_question}
  
  Assistant: {response}
  

Training Procedure

• Hardware: Single GPU with 4GB VRAM optimization
• Method: QLoRA (Quantized Low-Rank Adaptation)
• Base Quantization: 4-bit NF4 format with double quantization
• Memory Optimizations:
  • Gradient Checkpointing: Enabled
  • Mixed Precision (fp16)
  • 8-bit Adam optimizer
  • Gradient accumulation
• Training Progress:
  • Evaluation every 50 steps
  • Model checkpoints every 200 steps
  • TensorBoard logging enabled

Hyperparameters

• Training Configuration:

  • Epochs: 2
  • Batch Size: 1 (per device)
  • Gradient Accumulation Steps: 16
  • Effective Batch Size: 16
  • Learning Rate: 1e-4
  • Warmup Steps: 50

• LoRA Settings:

  • Rank (r): 16
  • Alpha: 32
  • Target Modules:
    • Attention: q_proj, k_proj, v_proj, o_proj
    • FFN: gate_proj, up_proj, down_proj
  • Dropout: 0.05
  • Bias: none
  • Task Type: CAUSAL_LM

Hardware

NVIDIA RTX 3050 with 4GB VRAM

Limitations

• Limited to chemistry domain knowledge
• Performance depends on base model capabilities
• May require 4GB+ VRAM for inference with quantization
• Responses should be verified for accuracy

Citation

If you use this model, please cite:

@misc{llama-chemistry-adapter,
  author = {Akshat Rai Laddha},
  title = {Chemistry QLoRA Adapter for Llama-3.2-3B},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
}

Framework versions

• PEFT 0.17.1