| 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]) | |
| ``` |