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
```

Does not generate text

by russellsparadox - opened Apr 7, 2023

Discussion

russellsparadox

Apr 7, 2023

All the time I use the model, it generates only <|endoftext|>. How can I fix it?

from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, GPTNeoXForCausalLM, AutoTokenizer

checkpoint = "OpenAssistant/oasst-sft-1-pythia-12b"

tokenizer = AutoTokenizer.from_pretrained(checkpoint, cache_dir='models_hf')
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True, cache_dir='models_hf')

message = "Hello, I am"
inp = "<|prompter|>"+message+"<|endoftext|><|assistant|>"
data = tokenizer([inp], return_tensors="pt")
data = {k: v.to(model.device) for k, v in data.items() if k in ("input_ids", "attention_mask")}
outputs = model.generate(**data)
print(tokenizer.decode(outputs[0]))

<|prompter|>Hello, I am<|endoftext|><|assistant|><|endoftext|>