Instructions to use OpenAssistant/pythia-12b-sft-v8-7k-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenAssistant/pythia-12b-sft-v8-7k-steps with Transformers:

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

pipe = pipeline("text-generation", model="OpenAssistant/pythia-12b-sft-v8-7k-steps")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/pythia-12b-sft-v8-7k-steps")
model = AutoModelForCausalLM.from_pretrained("OpenAssistant/pythia-12b-sft-v8-7k-steps")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenAssistant/pythia-12b-sft-v8-7k-steps with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenAssistant/pythia-12b-sft-v8-7k-steps"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenAssistant/pythia-12b-sft-v8-7k-steps",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenAssistant/pythia-12b-sft-v8-7k-steps

SGLang

How to use OpenAssistant/pythia-12b-sft-v8-7k-steps 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/pythia-12b-sft-v8-7k-steps" \
    --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/pythia-12b-sft-v8-7k-steps",
		"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/pythia-12b-sft-v8-7k-steps" \
        --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/pythia-12b-sft-v8-7k-steps",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenAssistant/pythia-12b-sft-v8-7k-steps with Docker Model Runner:
```
docker model run hf.co/OpenAssistant/pythia-12b-sft-v8-7k-steps
```

Special tokens support?

by gsaivinay - opened May 11, 2023

Discussion

gsaivinay

May 11, 2023

Hello,

Thanks for this awesome model.

Does this iteration support special tokens?

<|system|>
<|assistant|>
<|prefix_begin|>
<|prefix_end|>
<|prompter|>

if so, can we have some examples on how to use?

ThatOneShortGuy

May 15, 2023

Yes, it does seem to support those special tokens. According to MESSAGE_AND_TOKEN_FORMAT, it doesn't support all of them. However, I have used them in my projects so far and it has been working well. This is what I've done:
<|system|> - Used to let the model know what it is supposed to do. I don't know if you are meant to include the <|endoftext|> token afterwards, but it seems to be working well for me so far.
Ex: <|system|>You are an AI that summarizes a conversation in as few words as possible.<|endoftext|>

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