Instructions to use DAMO-NLP-MT/polylm-multialpaca-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DAMO-NLP-MT/polylm-multialpaca-13b with Transformers:

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

pipe = pipeline("text-generation", model="DAMO-NLP-MT/polylm-multialpaca-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-MT/polylm-multialpaca-13b")
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-MT/polylm-multialpaca-13b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DAMO-NLP-MT/polylm-multialpaca-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DAMO-NLP-MT/polylm-multialpaca-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DAMO-NLP-MT/polylm-multialpaca-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DAMO-NLP-MT/polylm-multialpaca-13b

SGLang

How to use DAMO-NLP-MT/polylm-multialpaca-13b 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 "DAMO-NLP-MT/polylm-multialpaca-13b" \
    --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": "DAMO-NLP-MT/polylm-multialpaca-13b",
		"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 "DAMO-NLP-MT/polylm-multialpaca-13b" \
        --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": "DAMO-NLP-MT/polylm-multialpaca-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DAMO-NLP-MT/polylm-multialpaca-13b with Docker Model Runner:
```
docker model run hf.co/DAMO-NLP-MT/polylm-multialpaca-13b
```

polylm-multialpaca-13b

Commit History

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Update README.md

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Update README.md

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update readme

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add demo scripts

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xiangpeng.wxp commited on Jul 17, 2023

update tokenizer class

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upload model files

d44b248

xiangpeng.wxp commited on Jul 13, 2023

initial commit

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pemywei commited on Jul 13, 2023

Commit History

Adding `safetensors` variant of this model cbad5b8 verified

Update README.md b651219

Update README.md 0446a4a

update readme 4fe3edc

add demo scripts b31a023

update tokenizer class 1defe03

upload model files d44b248

initial commit 3e8c901

Adding `safetensors` variant of this model

cbad5b8
verified

Update README.md

b651219

Update README.md

0446a4a

update readme

4fe3edc

add demo scripts

b31a023

update tokenizer class

1defe03

upload model files

d44b248

initial commit

3e8c901