Instructions to use OpenAssistant/oasst-sft-1-pythia-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenAssistant/oasst-sft-1-pythia-12b with Transformers:

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

pipe = pipeline("text-generation", model="OpenAssistant/oasst-sft-1-pythia-12b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/oasst-sft-1-pythia-12b")
model = AutoModelForCausalLM.from_pretrained("OpenAssistant/oasst-sft-1-pythia-12b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenAssistant/oasst-sft-1-pythia-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenAssistant/oasst-sft-1-pythia-12b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenAssistant/oasst-sft-1-pythia-12b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenAssistant/oasst-sft-1-pythia-12b

SGLang

How to use OpenAssistant/oasst-sft-1-pythia-12b 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 "OpenAssistant/oasst-sft-1-pythia-12b" \
    --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": "OpenAssistant/oasst-sft-1-pythia-12b",
		"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 "OpenAssistant/oasst-sft-1-pythia-12b" \
        --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": "OpenAssistant/oasst-sft-1-pythia-12b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenAssistant/oasst-sft-1-pythia-12b with Docker Model Runner:
```
docker model run hf.co/OpenAssistant/oasst-sft-1-pythia-12b
```

How can I run the model with multiple GPUs?

by eeyrw - opened Mar 16, 2023

Discussion

eeyrw

Mar 16, 2023

Such as 12G*8?

eeyrw changed discussion title from How can I the model with multiple GPUs? to How can I run the model with multiple GPUs? Mar 16, 2023

olivierdehaene

OpenAssistant org Mar 16, 2023

You can use text-generation-inference:

model=OpenAssistant/oasst-sft-1-pythia-12b
num_shard=4 # number of GPUs you want to use
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --num-shard $num_shard

Then you can query the model with text-generation:

from text_generation import Client

client = Client("http://localhost:8080")

client.generate("<|prompter|>What's the Earth total population<|endoftext|><|assistant|>").generated_text

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