license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
library_name: peft
pipeline_tag: text-generation
language:
- en
tags:
- crystallography
- structural-biology
- lora
- peft
Mistral 7B — crystallography LoRA adapter
LoRA adapter for mistralai/Mistral-7B-Instruct-v0.3, fine-tuned on confirmed
question-and-answer pairs from the CCP4BB, CCP-EM, phenixbb and COOT
mailing lists.
Training
| Method | LoRA, r=64, α=128, dropout=0.05 |
| Learning rate | 2.675e-05 |
| Max length | 2048 tokens |
| Epochs | 2, early stopping |
| Seed | 1 |
Evaluation
BERTScore F1 against held-out confirmed answers (433 questions,
roberta-large, score(prediction, reference)):
0.8367 → 0.8536.
BERTScore measures overlap with the reference wording, not factual correctness; the paper reports three LLM judges alongside it.
Use
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.3", device_map="auto", dtype="bfloat16")
model = PeftModel.from_pretrained(model, "ealharbi/crystallography-cryoem-mistral-7b-lora")
The system prompt used in training was:
You are a helpful crystallography assistant. Be concise and precise.
The training pairs are single-turn, so ask one self-contained question at a time; multi-turn prompts fall outside the fine-tuning format.
Caveat
Answers are generated and may be wrong. Verify anything consequential against the program documentation and the primary literature.