Instructions to use lmsys/vicuna-7b-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmsys/vicuna-7b-v1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lmsys/vicuna-7b-v1.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.1") model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.1") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lmsys/vicuna-7b-v1.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lmsys/vicuna-7b-v1.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lmsys/vicuna-7b-v1.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lmsys/vicuna-7b-v1.1
- SGLang
How to use lmsys/vicuna-7b-v1.1 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 "lmsys/vicuna-7b-v1.1" \ --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": "lmsys/vicuna-7b-v1.1", "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 "lmsys/vicuna-7b-v1.1" \ --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": "lmsys/vicuna-7b-v1.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lmsys/vicuna-7b-v1.1 with Docker Model Runner:
docker model run hf.co/lmsys/vicuna-7b-v1.1
Update chat template
#9
by tjtanaa - opened
No description provided.
weichiang changed pull request status to closed
Sorry for submitting a PR with so much changes.
The tokenizer_config.json content was actually auto generated from
tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.1")
tokenizer.chat_template = <jinja2 content>
tokenizer.push_to_hub(
repo_id=st.session_state['repo_id'],
commit_message=commit_message_text_input,
private=to_private_checkbox,
token=access_token_no_cache,
create_pr=create_pr_checkbox)
from HuggingSpace.
I think the changes to tokenizer_config.json should be correct as I didn't modify anything other than just adding the chat_template attributes. (Let me know about your thoughts)
As a LLM model user, it is not easy to keep track so many prompt template. This is the UI that I have created to hope that all existing Model Repo could use the new chat_template huggingface feature.
tjtanaa changed pull request status to open