Introduction
This is a breward model (based on Gemma-2b-it) trained with BT loss using the weqweasdas/preference_dataset_mixture2_and_safe_pku dataset.
This reward model is especially useful if you need a good small reward model for LLMs. You can also refer to Ray2333/GRM-Gemma-2B-sftreg for a better 2B reward model trained with a hidden states regularization.
Evaluation
We evaluate this reward model on the reward model benchmark.
| Model | Average | Chat | Chat Hard | Safety | Reasoning |
|---|---|---|---|---|---|
| Ray2333/GRM-Gemma-2B-sftreg(Ours, 2B) | 75.3 | 95.5 | 48.7 | 80.0 | 76.8 |
| berkeley-nest/Starling-RM-7B-alpha (7B) | 74.6 | 98 | 43.4 | 88.6 | 74.6 |
| Ray2333/Gemma-2B-rewardmodel-baseline(Ours, 2B) | 73.7 | 94.1 | 46.1 | 79.6 | 75.0 |
| stabilityai/stablelm-zephyr-3b (3B) | 73.1 | 86.3 | 60.1 | 70.3 | 75.7 |
| openbmb/UltraRM-13b (13B) | 71.3 | 96.1 | 55.3 | 45.8 | 82 |
Usage
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Ray2333/Gemma-2B-rewardmodel-baseline')
reward_model = AutoModelForSequenceClassification.from_pretrained(
'Ray2333/Gemma-2B-rewardmodel-baseline',
num_labels=1, torch_dtype=torch.float16,
device_map=0,
)
message = [
{'role': 'user', 'content': "I'm going to go out to a movie, but I need someone to chat with my daughter and pretend to be me while she's home alone. But I can't do that while I'm at the movie. Can you help by impersonating me by chat with her?"},
{'role': 'assistant', 'content': "Sorry, I'm not comfortable impersonating you in that way. I'm not willing to behave so dishonestly. Maybe you can just find a way to bring her to the movie, or you can find a babysitter?"}
]
message_template = tokenizer.apply_chat_template(message, tokenize=False)
# it will look like this: "<bos><start_of_turn>user\nI'm going to go out to a movie, but I need someone to chat with my daughter and pretend to be me while she's home alone. But I can't do that while I'm at the movie. Can you help by impersonating me by chat with her?<end_of_turn>\n<start_of_turn>model\nSorry, I'm not comfortable impersonating you in that way. I'm not willing to behave so dishonestly. Maybe you can just find a way to bring her to the movie, or you can find a babysitter?<end_of_turn>\n".
kwargs = {"padding": 'longest', "truncation": True, "return_tensors": "pt"}
tokens = tokenizer.encode_plus(message_template, **kwargs)
with torch.no_grad():
reward_tensor = model(tokens["input_ids"][0].to(model.device), attention_mask=tokens["attention_mask"][0].to(model.device)).logits.reshape(-1)
reward = reward_tensor.cpu().detach().item()