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

Libraries

How to use aws-neuron/zephyr-7b-beta-neuron with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aws-neuron/zephyr-7b-beta-neuron")
model = AutoModelForCausalLM.from_pretrained("aws-neuron/zephyr-7b-beta-neuron")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aws-neuron/zephyr-7b-beta-neuron with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aws-neuron/zephyr-7b-beta-neuron"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aws-neuron/zephyr-7b-beta-neuron",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aws-neuron/zephyr-7b-beta-neuron

SGLang

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

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

Update README.md

by jburtoft - opened Jan 12, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

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README.md +3 -3

README.md CHANGED Viewed

@@ -43,7 +43,7 @@ This model has been compiled to run on an inf2.xlarge (the smallest Inferentia2
 ## Set up the environment
-First, use the [DLAMI image from Hugging Face](https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2).  It has most of the utilities and drivers preinstalled.  However, you will need to update transformers-neruonx from the source to get Mistral support.
 ```
@@ -52,9 +52,9 @@ python -m pip install git+https://github.com/aws-neuron/transformers-neuronx.git
 ## Running inference from this repository
-If you want to run a quick test or if the exact model you want to use is [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), you can run it directly using the steps below.  Otherwise, jump to the Compilation of other Mistral versions section.
-First, you will need a local copy of the library.  This is because one of the nice things that the Hugging Face optimum library does is abstract local loads from repository loads.  However, Mistral inference isn't supported yet.
 ```

 ## Set up the environment
+First, use the [DLAMI image from Hugging Face](https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2).  It has most of the utilities and drivers preinstalled.  However, you will need to update transformers-neruonx from the source to get Mistral/Zephyr support.
 ```
 ## Running inference from this repository
+If you want to run a quick test or if the exact model you want to use is [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), you can run it directly using the steps below.  Otherwise, jump to the Compilation of other Mistral/Zephyr versions section.
+First, you will need a local copy of the library.  This is because one of the nice things that the Hugging Face optimum library does is abstract local loads from repository loads.  However, Mistral/Zephyr inference isn't supported yet.
 ```