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from swift.llm import (InferRequest, Model, ModelGroup, ModelMeta, PtEngine, RequestConfig, TemplateMeta, |
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get_model_tokenizer_with_flash_attn, register_model, register_template) |
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register_template( |
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TemplateMeta( |
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template_type='custom', |
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prefix=['<extra_id_0>System\n{{SYSTEM}}\n'], |
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prompt=['<extra_id_1>User\n{{QUERY}}\n<extra_id_1>Assistant\n'], |
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chat_sep=['\n'])) |
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register_model( |
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ModelMeta( |
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model_type='custom', |
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model_groups=[ |
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ModelGroup([Model('AI-ModelScope/Nemotron-Mini-4B-Instruct', 'nvidia/Nemotron-Mini-4B-Instruct')]) |
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], |
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template='custom', |
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get_function=get_model_tokenizer_with_flash_attn, |
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ignore_patterns=['nemo'])) |
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if __name__ == '__main__': |
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infer_request = InferRequest(messages=[{'role': 'user', 'content': 'who are you?'}]) |
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request_config = RequestConfig(max_tokens=512, temperature=0) |
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engine = PtEngine('AI-ModelScope/Nemotron-Mini-4B-Instruct') |
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response = engine.infer([infer_request], request_config) |
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swift_response = response[0].choices[0].message.content |
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engine.default_template.template_backend = 'jinja' |
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response = engine.infer([infer_request], request_config) |
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jinja_response = response[0].choices[0].message.content |
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assert swift_response == jinja_response, (f'swift_response: {swift_response}\njinja_response: {jinja_response}') |
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print(f'response: {swift_response}') |
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