LLaMA-3.1-8B Pharmaceutical Compliance QLoRA
Fine-tuned LLaMA-3.1-8B-Instruct on 500 FDA pharmaceutical compliance instruction pairs using QLoRA + Unsloth.
Model Details
| Property | Value |
|---|---|
| Base Model | meta-llama/Meta-Llama-3.1-8B-Instruct |
| Method | QLoRA 4-bit NF4 + LoRA rank 64 |
| Framework | Unsloth (2x faster, 60% less VRAM) |
| Domain | FDA CFR Title 21, GMP, ICH guidelines |
| Training Samples | 450 |
| Epochs | 3 |
| GPU | A100 40GB |
Domain Coverage
- 21 CFR Part 210/211 (cGMP)
- 21 CFR Part 201 (Drug labeling)
- ICH Q8/Q9/Q10 (Pharmaceutical quality)
- OOS investigations, CAPA writing
- Deviation reports, process validation
- Bioequivalence requirements
Usage
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="nihanthnaidu007/llama-3.1-8b-pharma-compliance-qlora-v4",
max_seq_length=1024,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
Author
Nihanth Naidu Kalisetti — AI Engineer W&B Run: https://wandb.ai/nihanthnaidu-kalisetti-long-island-university/llama-pharma-qlora/runs/vt1w00g4
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Model tree for nihanthnaidu007/llama-3.1-8b-pharma-compliance-qlora-v4
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct