Instructions to use McGill-NLP/Llama-2-7b-chat-weblinx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use McGill-NLP/Llama-2-7b-chat-weblinx with Transformers:

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

pipe = pipeline("text-generation", model="McGill-NLP/Llama-2-7b-chat-weblinx")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("McGill-NLP/Llama-2-7b-chat-weblinx", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use McGill-NLP/Llama-2-7b-chat-weblinx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "McGill-NLP/Llama-2-7b-chat-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/Llama-2-7b-chat-weblinx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/McGill-NLP/Llama-2-7b-chat-weblinx

SGLang

How to use McGill-NLP/Llama-2-7b-chat-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/Llama-2-7b-chat-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/Llama-2-7b-chat-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/Llama-2-7b-chat-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/Llama-2-7b-chat-weblinx",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use McGill-NLP/Llama-2-7b-chat-weblinx with Docker Model Runner:
```
docker model run hf.co/McGill-NLP/Llama-2-7b-chat-weblinx
```

Llama-2-7b-chat-weblinx / pytorch_model.bin

xhluca

Add model, tokenizer, and config files, input records.

b50f093 almost 3 years ago

History Blame Contribute Delete

13.5 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 3ede023d0233af14498ce74e8e1602f1c207c5756288058775f585eb03358eed
Size of remote file:: 13.5 GB
SHA256:: 43c643a481fd877b0a1436108465664d390aea9ff6bb39a3e78762512b21ca7d

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

McGill-NLP
/

Llama-2-7b-chat-weblinx

Detected Pickle imports (5)

Xet Pointer Details