Fava Model

🚀 FAVA - Verification Model

FAVA is a verification model designed to perform specific verification tasks.

🚀 Quick Start

To get started with FAVA, you can use the following code to import the necessary libraries and run the verification process.

import torch
import vllm
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model = vllm.LLM(model="fava-uw/fava-model")
sampling_params = vllm.SamplingParams(
  temperature=0,
  top_p=1.0,
  max_tokens=1024,
)

INPUT = "Read the following references:\n{evidence}\nPlease identify all the errors in the following text using the information in the references provided and suggest edits if necessary:\n[Text] {output}\n[Edited] "

output = "" # add your passage to verify
evidence = "" # add a piece of evidence
prompts = [INPUT.format_map({"evidence": evidence, "output": output})]
outputs = model.generate(prompts, sampling_params)
outputs = [it.outputs[0].text for it in outputs]
print(outputs[0])

📄 License

This project is licensed under the MIT License.