unh-academic-integrity-policy-560m

This model is a fine-tuned version of bigscience/bloomz-560m designed to generate academic policy-aligned text. It incorporates Retrieval-Augmented Generation (RAG) and Parameter-Efficient Fine-Tuning (PEFT) techniques to improve response relevance and reduce memory usage.

Intended Use

• Academic policy Q/A text-generation
• Text generation with contextual grounding
• Research use in NLP and LLM alignment

Training Details

• Learning rate: 2e-05
• Batch size: 2 (train), 8 (eval)
• Epochs: 2
• Optimizer: Adam
• Gradient accumulation: 4
• Precision: Mixed (AMP)
• Scheduler: Linear decay

Deployment

Optimized for deployment with Hugging Face Inference Endpoints. Also supports:

• Amazon SageMaker
• Azure ML
• Friendli Inference

Deployment Status

This model is not currently deployed on a public inference provider. You can deploy it using Hugging Face Inference Endpoints or export it to services like Amazon SageMaker or Azure ML.

Framework Versions

• Transformers: 4.35.2
• PyTorch: 2.1.0+cu121
• Datasets: 2.15.0
• Tokenizers: 0.15.0