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1## Step 1. Prepare the QAnything project and download local Embedding/Rerank models.
2
3git clone https://github.com/netease-youdao/QAnything.git
4cd /path/to/QAnything && mkdir -p tmp && cd tmp
5git lfs install
6git clone https://huggingface.co/netease-youdao/QAnything
7unzip QAnything/models.zip
8cd - && mv tmp/models .
9
10## Step 2. Download the public LLM model (e.g., Qwen-7B-QAnything) and save to "/path/to/QAnything/assets/custom_models"
11cd /path/to/QAnything/assets/custom_models
12git clone https://huggingface.co/netease-youdao/Qwen-7B-QAnything
13
14## Step 3. Execute the service startup command. Here we use "-b hf" to specify the Huggingface transformers backend.
15## Here we use "-b hf" to specify the transformers backend that will load model in 8 bits but do bf16 inference as default for saving VRAM.
16cd /path/to/QAnything
17bash ./run.sh -c local -i 0 -b hf -m Qwen-7B-QAnything -t qwen-7b-qanything1
2## Step 1. Prepare the QAnything project and download local Embedding/Rerank models.
3
4git clone https://github.com/netease-youdao/QAnything.git
5cd /path/to/QAnything && mkdir -p tmp && cd tmp
6git lfs install
7git clone https://huggingface.co/netease-youdao/QAnything
8unzip QAnything/models.zip
9cd - && mv tmp/models .
10
11## Step 2. Download the public LLM model (e.g., Qwen-7B-QAnything) and save to "/path/to/QAnything/assets/custom_models"
12cd /path/to/QAnything/assets/custom_models
13git clone https://huggingface.co/netease-youdao/Qwen-7B-QAnything
14
15## Step 3. Execute the service startup command. Here we use "-b vllm" to specify the Huggingface transformers backend.
16## Here we use "-b vllm" to specify the vllm backend that will do bf16 inference as default.
17## Note you should adjust the gpu_memory_utilization yourself according to the model size to avoid out of memory (e.g., gpu_memory_utilization=0.81 is set default for 7B. Here, gpu_memory_utilization is set to 0.85 by "-r 0.85").
18cd /path/to/QAnything
19bash ./run.sh -c local -i 0 -b vllm -m Qwen-7B-QAnything -t qwen-7b-qanything -p 1 -r 0.85
20