from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model_path = "reciprocate/mistral-7b-rm"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
reward_fn = pipeline("text-classification", model=model, tokenizer=tokenizer, truncation=True, batch_size=8, max_length=4096, device=0)

chats = [[
    {"role": "user", "content": "When was the battle at Waterloo?"},
    {"role": "assistant", "content": "I think it was in 1983, but please double-check that when you have a chance."}
], [
    {"role": "user", "content": "When was the battle at Waterloo?"},
    {"role": "assistant", "content": "The battle at Waterloo took place on June 18, 1815."}
]]

output = reward_fn([tokenizer.apply_chat_template(chat, tokenize=False) for chat in chats])
scores = [x["score"] for x in output]
scores

>>> [0.2586347758769989, 0.6663259267807007]

# optionally normalize with the mean and std computed on the training data
scores = (np.array(scores) - 2.01098) / 1.69077

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