MedinaArmando/llama-3.2-1b-cfr-merged_II

Up and running in Hugging Face Space using 2 virtual cpu's and 16 GB RAM!! CFR-FineTuned_III

Llama-3.2-1B Fine-tuned on the Code of Federal Regulations (CFR)

This is a fine-tuned version of meta-llama/Meta-Llama-3.2-1B trained on all sections from the United States Code of Federal Regulations (CFR). The goal: provide a specialized assistant for navigating and answering questions about U.S. federal regulations.

Model Description

• —Base Model: Llama-3.2-1B
• —Method: QLoRA, 4-bit quantization
• —Dataset: Custom, parsed from CFR XML (Titles 1-50)
• —Epochs: 3
• —Tokens Seen: ~243M
• —Final Training Loss: 1.267
• —Mean Token Accuracy: 0.739
• —Training Time: ~5h 17m

Hardware/Environment: Training was conducted on Modal using a single NVIDIA H200 GPU. Training speed: ~1.10 steps/sec, 35 samples/sec.

Note: This loss is typical for a Llama-3 1B model on legal/complex text. For comparison: random output would yield >2.0; perfect memorization of a small dataset would yield <1.0. This is in the “actually learned something useful” range for this setup.

Intended Uses & Limitations

Intended Uses

• —Regulatory Q&A
• —Summarization of CFR text
• —Text generation related to U.S. federal regulations

Limitations

• —NOT a substitute for legal advice. Output may be incorrect or outdated (data as of 2024-06-25).
• —Can hallucinate—don’t trust answers without checking against the source.
• —Validation/test loss is not reported here (evaluate on your own task/data before using in production).

How to Use

You can use this model directly with the transformers library.