NOTE: THIS IS THE RANDOM SAMPLING VERSION WEPO. DOM TREE DISTANCE-BASED VERSION WILL BE RELEASED SOON.
Below is the reference code for inference. First load the tokenizer and the model.
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("KLGR123/WEPO-mistral-7b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("KLGR123/WEPO-mistral-7b", trust_remote_code=True).to('cuda:0')
Run a test-demo with random input.
messages = [
{"role": "system", "content": "You are a web navigation intelligence who interacts with webpage environments to achieve human user intent."},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=128,
eos_token_id=terminators,
do_sample=True,
temperature=0.2,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
output = tokenizer.decode(response, skip_special_tokens=True)
output