Instructions to use BAAI/AquilaDense-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BAAI/AquilaDense-7B with Transformers:

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

pipe = pipeline("text-generation", model="BAAI/AquilaDense-7B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("BAAI/AquilaDense-7B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BAAI/AquilaDense-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BAAI/AquilaDense-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BAAI/AquilaDense-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BAAI/AquilaDense-7B

SGLang

How to use BAAI/AquilaDense-7B 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 "BAAI/AquilaDense-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BAAI/AquilaDense-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "BAAI/AquilaDense-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BAAI/AquilaDense-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BAAI/AquilaDense-7B with Docker Model Runner:
```
docker model run hf.co/BAAI/AquilaDense-7B
```

ldwang commited on Jul 13

Commit

bbd91b0

verified ·

1 Parent(s): f4ca900

Update README.md

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@@ -172,4 +172,12 @@ Our paper, detailing the efficient training methods for MoE models using Scale-U
   author={{Language Foundation Model \& Software Team, Beijing Academy of Artificial Intelligence (BAAI)}},
   journal={arXiv preprint arXiv:2406.XXXX},
   year={2024}
-}

   author={{Language Foundation Model \& Software Team, Beijing Academy of Artificial Intelligence (BAAI)}},
   journal={arXiv preprint arXiv:2406.XXXX},
   year={2024}
+}
+```
+# Acknowledgements
+This work is supported by the National Science and Technology Major Project (No. 2022ZD0116300).
+本项目受新一代人工智能国家科技重大专项（No. 2022ZD0116300）支持。