Download examples/deploy/lora/client.py from BBBBCHAN/StreamDelta: direct link, hf CLI and curl.
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https://huggingface.co/datasets/BBBBCHAN/StreamDelta/resolve/main/examples/deploy/lora/client.py
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1.07 kB
| from swift.llm import InferClient, InferRequest, RequestConfig | |
| def infer_multilora(engine: InferClient, infer_request: InferRequest): | |
| # Dynamic LoRA | |
| models = engine.models | |
| print(f'models: {models}') | |
| request_config = RequestConfig(max_tokens=512, temperature=0) | |
| # use lora1 | |
| resp_list = engine.infer([infer_request], request_config, model=models[1]) | |
| response = resp_list[0].choices[0].message.content | |
| print(f'lora1-response: {response}') | |
| # origin model | |
| resp_list = engine.infer([infer_request], request_config, model=models[0]) | |
| response = resp_list[0].choices[0].message.content | |
| print(f'response: {response}') | |
| # use lora2 | |
| resp_list = engine.infer([infer_request], request_config, model=models[2]) | |
| response = resp_list[0].choices[0].message.content | |
| print(f'lora2-response: {response}') | |
| if __name__ == '__main__': | |
| engine = InferClient(host='127.0.0.1', port=8000) | |
| infer_request = InferRequest(messages=[{'role': 'user', 'content': 'who are you?'}]) | |
| infer_multilora(engine, infer_request) | |