Instructions to use OpenAssistant/llama2-70b-oasst-sft-v10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenAssistant/llama2-70b-oasst-sft-v10 with Transformers:

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

pipe = pipeline("text-generation", model="OpenAssistant/llama2-70b-oasst-sft-v10")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/llama2-70b-oasst-sft-v10")
model = AutoModelForCausalLM.from_pretrained("OpenAssistant/llama2-70b-oasst-sft-v10")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenAssistant/llama2-70b-oasst-sft-v10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenAssistant/llama2-70b-oasst-sft-v10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenAssistant/llama2-70b-oasst-sft-v10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenAssistant/llama2-70b-oasst-sft-v10

SGLang

How to use OpenAssistant/llama2-70b-oasst-sft-v10 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/llama2-70b-oasst-sft-v10" \
    --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/llama2-70b-oasst-sft-v10",
		"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/llama2-70b-oasst-sft-v10" \
        --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/llama2-70b-oasst-sft-v10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenAssistant/llama2-70b-oasst-sft-v10 with Docker Model Runner:
```
docker model run hf.co/OpenAssistant/llama2-70b-oasst-sft-v10
```

Question about sequence length.

by gsaivinay - opened Aug 23, 2023

Discussion

gsaivinay

Aug 23, 2023

Hello,

Thanks for this awesome work.

I'd like to ask if this model is finetuned with same 4k sequence length, and is there any possibility to extend to 8k length given that it performs better in coding tasks.

andreaskoepf

OpenAssistant org Aug 23, 2023

This comment has been hidden

gsaivinay

Aug 25, 2023

I see config.json is now modified with 4k length, might be a configuration issues.

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