Instructions to use HuggingFaceH4/zephyr-7b-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceH4/zephyr-7b-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta") model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HuggingFaceH4/zephyr-7b-beta with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceH4/zephyr-7b-beta" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceH4/zephyr-7b-beta", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/HuggingFaceH4/zephyr-7b-beta
- SGLang
How to use HuggingFaceH4/zephyr-7b-beta 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 "HuggingFaceH4/zephyr-7b-beta" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceH4/zephyr-7b-beta", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "HuggingFaceH4/zephyr-7b-beta" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceH4/zephyr-7b-beta", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use HuggingFaceH4/zephyr-7b-beta with Docker Model Runner:
docker model run hf.co/HuggingFaceH4/zephyr-7b-beta
Zephyr-7B-alpha on LLM leaderboard, why not Zephyr-7b-beta?
I'd love to get the LLM leaderboard's opinion on how Zephyr-7B-beta compares to other Mistral finetunes. Could we get it submitted for evaluation?
Thanks for the reminder @YokaiKoibito ! I think we do have the evals, but forgot to make them public. Maybe @clefourrier or @SaylorTwift can add them now?
It's not my model, and anyway, it looks like it's already in the Pending Evaluation Queue (twice).
You can submit models that are not yours as long as they are public :)
If it's pending, it will get evaluated in the next weeks, since we are catching up on the queue now! And models appearing two times have usually been submitted at different precisions (it's explained in our FAQ if you want more details)
Closing then, since it's going to be evaluated!