Instructions to use BAAI/AquilaCode-py with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/AquilaCode-py with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BAAI/AquilaCode-py")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("BAAI/AquilaCode-py", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use BAAI/AquilaCode-py with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BAAI/AquilaCode-py" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BAAI/AquilaCode-py", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BAAI/AquilaCode-py
- SGLang
How to use BAAI/AquilaCode-py 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/AquilaCode-py" \ --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": "BAAI/AquilaCode-py", "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 "BAAI/AquilaCode-py" \ --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": "BAAI/AquilaCode-py", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BAAI/AquilaCode-py with Docker Model Runner:
docker model run hf.co/BAAI/AquilaCode-py
Commit ·
7850d23
1
Parent(s): f01765f
Update README.md
Browse files
README.md
CHANGED
|
@@ -39,10 +39,10 @@ The additional details of the Aquila model will be presented in the official tec
|
|
| 39 |
We will continue to release improved versions of Aquila model as open source.
|
| 40 |
|
| 41 |
- 2023/07/14 :release v0.8
|
| 42 |
-
-
|
| 43 |
-
-
|
| 44 |
-
-
|
| 45 |
-
-
|
| 46 |
|
| 47 |
|
| 48 |
Aquila-7B v0.8 has shown improvements in the FlagEval large model evaluation ("Objective") compared to version 0.7. It achieved improvements of approximately 10.07% on MMLU_Chinese, 14.84% on TruthfulQA, and 7.94% on MMLU datasets. For detailed evaluation results, please refer to the website http://flageval.baai.ac.cn.
|
|
|
|
| 39 |
We will continue to release improved versions of Aquila model as open source.
|
| 40 |
|
| 41 |
- 2023/07/14 :release v0.8
|
| 42 |
+
- AquilaCode-mutil-01 md5: e202e5b82db773ea369fe843fef1c34c
|
| 43 |
+
- AquilaCode-mutil-02 md5: 3923b2b020e2af71755b11248076437f
|
| 44 |
+
- AquilaCode-Python-01 md5: e202e5b82db773ea369fe843fef1c34c
|
| 45 |
+
- AquilaCode-Python-02 md5: 3923b2b020e2af71755b11248076437f
|
| 46 |
|
| 47 |
|
| 48 |
Aquila-7B v0.8 has shown improvements in the FlagEval large model evaluation ("Objective") compared to version 0.7. It achieved improvements of approximately 10.07% on MMLU_Chinese, 14.84% on TruthfulQA, and 7.94% on MMLU datasets. For detailed evaluation results, please refer to the website http://flageval.baai.ac.cn.
|