FAVA, a verification model.

```
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])
```
{}
transformers pytorch tensorboard llama text-generation autotrain_compatible endpoints_compatible has_space text-generation-inference region:us