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

Libraries

How to use DataCanvas/Alaya-7B-Base with Transformers:

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

pipe = pipeline("text-generation", model="DataCanvas/Alaya-7B-Base", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DataCanvas/Alaya-7B-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DataCanvas/Alaya-7B-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DataCanvas/Alaya-7B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DataCanvas/Alaya-7B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DataCanvas/Alaya-7B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DataCanvas/Alaya-7B-Base

SGLang

How to use DataCanvas/Alaya-7B-Base 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 "DataCanvas/Alaya-7B-Base" \
    --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": "DataCanvas/Alaya-7B-Base",
		"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 "DataCanvas/Alaya-7B-Base" \
        --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": "DataCanvas/Alaya-7B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DataCanvas/Alaya-7B-Base with Docker Model Runner:
```
docker model run hf.co/DataCanvas/Alaya-7B-Base
```

ShellyYuJJ commited on Nov 20, 2023

Commit

9811d73

1 Parent(s): d2f31cd

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -1

README.md CHANGED Viewed

@@ -1,3 +1,9 @@
 # 九章元识 | DataCanvas Alaya
 GitHub: https://github.com/DataCanvasIO/Alaya
@@ -44,4 +50,4 @@ Alaya使用<a href="https://github.com/DataCanvasIO/Alaya/blob/main/LICENSE">Apa
     year = {2023},
     howpublished = {\url{https://github.com/DataCanvasIO/Alaya}},
 }
-```

+---
+license: apache-2.0
+language:
+- zh
+- en
+---
 # 九章元识 | DataCanvas Alaya
 GitHub: https://github.com/DataCanvasIO/Alaya
     year = {2023},
     howpublished = {\url{https://github.com/DataCanvasIO/Alaya}},
 }
+```