Instructions to use McGill-NLP/Sheared-LLaMA-2.7B-weblinx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use McGill-NLP/Sheared-LLaMA-2.7B-weblinx with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="McGill-NLP/Sheared-LLaMA-2.7B-weblinx")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("McGill-NLP/Sheared-LLaMA-2.7B-weblinx", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use McGill-NLP/Sheared-LLaMA-2.7B-weblinx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "McGill-NLP/Sheared-LLaMA-2.7B-weblinx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "McGill-NLP/Sheared-LLaMA-2.7B-weblinx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/McGill-NLP/Sheared-LLaMA-2.7B-weblinx

SGLang

How to use McGill-NLP/Sheared-LLaMA-2.7B-weblinx with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "McGill-NLP/Sheared-LLaMA-2.7B-weblinx" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "McGill-NLP/Sheared-LLaMA-2.7B-weblinx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "McGill-NLP/Sheared-LLaMA-2.7B-weblinx" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "McGill-NLP/Sheared-LLaMA-2.7B-weblinx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use McGill-NLP/Sheared-LLaMA-2.7B-weblinx with Docker Model Runner:
```
docker model run hf.co/McGill-NLP/Sheared-LLaMA-2.7B-weblinx
```

Configuration Parsing Warning:Config file config.json cannot be fetched (too big)

Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)

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

Xing Han Lù*, Zdeněk Kasner*, Siva Reddy

Quickstart

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-2.7B-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.