Instructions to use aws-neuron/SOLAR-10.7B-v1.0-neuron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aws-neuron/SOLAR-10.7B-v1.0-neuron with Transformers:

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

pipe = pipeline("text-generation", model="aws-neuron/SOLAR-10.7B-v1.0-neuron")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aws-neuron/SOLAR-10.7B-v1.0-neuron")
model = AutoModelForCausalLM.from_pretrained("aws-neuron/SOLAR-10.7B-v1.0-neuron", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aws-neuron/SOLAR-10.7B-v1.0-neuron with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aws-neuron/SOLAR-10.7B-v1.0-neuron"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aws-neuron/SOLAR-10.7B-v1.0-neuron",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aws-neuron/SOLAR-10.7B-v1.0-neuron

SGLang

How to use aws-neuron/SOLAR-10.7B-v1.0-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/SOLAR-10.7B-v1.0-neuron" \
    --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": "aws-neuron/SOLAR-10.7B-v1.0-neuron",
		"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 "aws-neuron/SOLAR-10.7B-v1.0-neuron" \
        --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": "aws-neuron/SOLAR-10.7B-v1.0-neuron",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aws-neuron/SOLAR-10.7B-v1.0-neuron with Docker Model Runner:
```
docker model run hf.co/aws-neuron/SOLAR-10.7B-v1.0-neuron
```

jburtoft commited on Feb 12, 2024

Commit

63373d1

verified ·

1 Parent(s): 7147c40

Update README.md

Browse files

Removing 2.16 language

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README.md +7 -18

README.md CHANGED Viewed

@@ -28,26 +28,15 @@ Please refer to the 🤗 `optimum-neuron` [documentation](https://huggingface.co
 ## 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, but hasn't been updated to 2.16 as of 1/13/24.
-However, you will need version 2.16 to use these binaries. 2.16 shows a significant performance increase over 2.15 for Llama based models.
-The commands below will update your 2.15 libraries to 2.16.
-```
-sudo apt-get update -y \
- && sudo apt-get install -y --no-install-recommends \
-    aws-neuronx-dkms=2.15.9.0 \
-    aws-neuronx-collectives=2.19.7.0-530fb3064 \
-    aws-neuronx-runtime-lib=2.19.5.0-97e2d271b \
-    aws-neuronx-tools=2.16.1.0
-pip3 install --upgrade \
-    neuronx-cc==2.12.54.0 \
-    torch-neuronx==1.13.1.1.13.0 \
-    transformers-neuronx==0.9.474 \
-    --extra-index-url=https://pip.repos.neuron.amazonaws.com
-pip3 install git+https://github.com/huggingface/optimum-neuron.git
-```
 ## Running inference from this repository

 ## 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, but hasn't been updated to 2.16 as of 1/13/24.
+(As of the 20240123 version, the Hugging Face DLAMI image has the updated 2.16 binaries)
+~~However, you will need version 2.16 to use these binaries. 2.16 shows a significant performance increase over 2.15 for Llama based models.~~
+~~The commands below will update your 2.15 libraries to 2.16.~~
+```<update commands removed>```
 ## Running inference from this repository