Instructions to use HuggingFaceH4/zephyr-7b-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceH4/zephyr-7b-alpha with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-alpha")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-alpha")
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

vLLM

How to use HuggingFaceH4/zephyr-7b-alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceH4/zephyr-7b-alpha"
# 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-alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HuggingFaceH4/zephyr-7b-alpha

SGLang

How to use HuggingFaceH4/zephyr-7b-alpha 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-alpha" \
    --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-alpha",
		"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-alpha" \
        --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-alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HuggingFaceH4/zephyr-7b-alpha with Docker Model Runner:
```
docker model run hf.co/HuggingFaceH4/zephyr-7b-alpha
```

Model answering with all newlines?

#19

by jamesbraza - opened Oct 17, 2023

Discussion

jamesbraza

Oct 17, 2023

from huggingface_hub import InferenceClient  # huggingface-hub[inference]==0.17.3

client = InferenceClient(model="HuggingFaceH4/zephyr-7b-alpha")
hi = client.text_generation(
    "Some choices are given below. It is provided in a numbered list (1 to 2), where"
    " each item in the list corresponds to a summary.\n---------------------\n(1)"
    " Provides information on cell lines like cell aliases, planes, and trains\n\n(2)"
    " Provides information on abc 123\n---------------------\nUsing only the choices above"
    " and not prior knowledge, return the choice that is most relevant to the question:"
    " 'What are the aliases for MLE12?'\n\n\nThe output should be ONLY JSON formatted"
    " as a JSON instance.\n\nHere is an example:\n[\n    {{\n        choice: 1,\n      "
    '  reason: "<insert reason for choice>"\n    }},\n    ...\n]\n'
)

Here is a prompt that leads to the model generating 20 \n newlines. What is the issue here, why would it do that?

lewtun

Hugging Face H4 org Oct 18, 2023

Hello @jamesbraza the model was trained with a chat template and you need to format your inputs this way to ensure the model terminates generation at the right place. See the README for an example on how to format the inputs :)

jamesbraza

Jan 11, 2024

Ah gotchu, and thank you!

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