Mistral-7B Document Summarizer

Official adapter · maintained by ModelForgeLab

Extractive-then-abstractive summarization tuned on long-form technical and legal documents. Reduces hallucination on dense source material.

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
base = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
model = PeftModel.from_pretrained(base, "modelforgelab/mistral-7b-doc-summary")

inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Compatibility

• peft>=0.10
• transformers>=4.40
• vLLM