from datasets import load_dataset
from huggingface_hub import snapshot_download
from transformers import pipeline

# Load validation split
valid = load_dataset("McGill-NLP/weblinx", split="validation")

# Download and load the templates
snapshot_download(
    "McGill-NLP/WebLINX", repo_type="dataset", allow_patterns="templates/*.txt", local_dir="./"
)
with open('templates/llama.txt') as f:
    template = f.read()

turn = valid[0]
turn_text = template.format(**turn)

# Load action model and input the text to get prediction
action_model = pipeline(
    model="McGill-NLP/Sheared-LLaMA-1.3B-weblinx", device=0, torch_dtype='auto'
)
out = action_model(turn_text, return_full_text=False, max_new_tokens=64, truncation=True)
pred = out[0]['generated_text']

print("Ref:", turn["action"])
print("Pred:", pred)

Original Model

This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.
Click here to access the original model.

License

This model is derived from LLaMA-2, which can only be used with the LLaMA 2 Community License Agreement. By using or distributing any portion or element of this model, you agree to be bound by this Agreement.

RichardErkhov/McGill-NLP_-_Sheared-LLaMA-1.3B-weblinx-awq

Original model description:

WebLINX: Real-World Website Navigation with Multi-Turn Dialogue

Quickstart

Original Model

License

RichardErkhov/
McGill-NLP_-_Sheared-LLaMA-1.3B-weblinx-awq