metadata
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
language:
- en
pipeline_tag: text-generation
GPT-2 Medium Fine-Tuned on Anthropic-hh Dataset
This repository houses a GPT-2 Medium model fine-tuned on the Anthropic-hh dataset. The fine-tuning process involved masking Human's utterances, with the loss computed exclusively on the Assistant's responses.
Model Information
- Base Model: GPT-2 Medium
- Training Data: Anthropic-hh dataset
- Fine-Tuning Approach: Supervised fine-tuning with a focus on Assistant's responses.
How to Use
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("RaushanTurganbay/GPT2_instruct_tuned")
model = GPT2LMHeadModel.from_pretrained("RaushanTurganbay/GPT2_instruct_tuned")
# Generate responses
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=1):
super().__init__()
self.stops = [stop.to("cuda") for stop in stops]
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
for stop in self.stops:
if torch.all((stop == input_ids[0][-len(stop):])).item():
return True
return False
def stopping_criteria(tokenizer, stop_words):
stop_words_ids = [tokenizer(stop_word, return_tensors='pt')['input_ids'].squeeze() for stop_word in stop_words]
stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
return stopping_criteria
# Generate responses
stopping = stopping_criteria(tokenizer, ["\n\nHuman:"])
prompt = "\n\nHuman: {your_instruction}\n\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, stopping_criteria=stopping, max_length=150)
print("Model Response:", tokenizer.batch_decode(outputs))