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")

# pip install -U transformers accelerate
# 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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

wonhosong commited on Jul 19, 2023

Commit

bbc09d9

1 Parent(s): bd23acb

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@@ -8,6 +8,6 @@ tags:
 - instruction
 pipeline_tag: text-generation
 ---
-Please refer to the model card of [upstage/llama-30b-instruct](https://huggingface.co/upstage/llama-30b-instruct) as this one is almost the same.
 **License** This model is under a Non-commercial Bespoke License and governed by the Meta license. You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform), but have either lost your copy of the weights or encountered issues converting them to the Transformers format.

 - instruction
 pipeline_tag: text-generation
 ---
+Please refer to the model card of <a href=https://huggingface.co/upstage/llama-30b-instruct target="_blank">upstage/llama-30b-instruct</a> as this one is almost the same.
 **License** This model is under a Non-commercial Bespoke License and governed by the Meta license. You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform), but have either lost your copy of the weights or encountered issues converting them to the Transformers format.