Instructions to use upstage/llama-30b-instruct-2048 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use upstage/llama-30b-instruct-2048 with Transformers:

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

pipe = pipeline("text-generation", model="upstage/llama-30b-instruct-2048")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("upstage/llama-30b-instruct-2048")
model = AutoModelForCausalLM.from_pretrained("upstage/llama-30b-instruct-2048")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use upstage/llama-30b-instruct-2048 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "upstage/llama-30b-instruct-2048"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "upstage/llama-30b-instruct-2048",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/upstage/llama-30b-instruct-2048

SGLang

How to use upstage/llama-30b-instruct-2048 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 "upstage/llama-30b-instruct-2048" \
    --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": "upstage/llama-30b-instruct-2048",
		"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 "upstage/llama-30b-instruct-2048" \
        --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": "upstage/llama-30b-instruct-2048",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use upstage/llama-30b-instruct-2048 with Docker Model Runner:
```
docker model run hf.co/upstage/llama-30b-instruct-2048
```

what is the prompt init to use

by pabloce - opened Jul 20, 2023

Discussion

pabloce

Jul 20, 2023

Hi,

Amazing work.

I come from TheBloke https://huggingface.co/TheBloke/upstage-llama-30b-instruct-2048-GGML
Just wanna test this with the right prompt init you can suggest the best result?

you are now on the first in the leaderboard.
woow.

Thanks.

Limerobot

upstage org Jul 24, 2023

Hi pabloce,

Thank you for your kind words and interest in our model.

We have recently updated our model card with a template as follows:

### System:
{System}

### User:
{User}

### Assistant:
{Assistant}

Thank you for your patience.

Limerobot changed discussion status to closed Jul 24, 2023

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment