Instructions to use BAAI/AquilaChat2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/AquilaChat2-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BAAI/AquilaChat2-7B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("BAAI/AquilaChat2-7B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BAAI/AquilaChat2-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BAAI/AquilaChat2-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BAAI/AquilaChat2-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BAAI/AquilaChat2-7B
- SGLang
How to use BAAI/AquilaChat2-7B 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/AquilaChat2-7B" \ --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/AquilaChat2-7B", "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/AquilaChat2-7B" \ --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/AquilaChat2-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BAAI/AquilaChat2-7B with Docker Model Runner:
docker model run hf.co/BAAI/AquilaChat2-7B
Update README.md
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README.md
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@@ -23,12 +23,22 @@ The additional details of the Aquila model will be presented in the official tec
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### 1. Inference
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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device = torch.device("cuda:0")
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model_info = "BAAI/AquilaChat2-7B"
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tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
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model.eval()
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model.to(device)
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text = "请给出10个要到北京旅游的理由。"
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### 1. Inference
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from transformers import BitsAndBytesConfig
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device = torch.device("cuda:0")
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model_info = "BAAI/AquilaChat2-7B"
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tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
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quantization_config=BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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)
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model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True, torch_dtype=torch.float16,
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# quantization_config=quantization_config, # Uncomment this line for 4bit quantization
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)
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model.eval()
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model.to(device)
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text = "请给出10个要到北京旅游的理由。"
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