|
|
| """
|
| Here is another way to register the model, by customizing the get_function.
|
|
|
| The get_function just needs to return the model + tokenizer/processor.
|
| """
|
| from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, PretrainedConfig, PreTrainedModel
|
|
|
| from swift.infer_engine import InferRequest, RequestConfig, TransformersEngine
|
| from swift.model import Model, ModelGroup, ModelLoader, ModelMeta, register_model
|
| from swift.template import TemplateMeta, register_template
|
| from swift.utils import Processor
|
|
|
| register_template(
|
| TemplateMeta(
|
| template_type='custom',
|
| prefix=['<extra_id_0>System\n{{SYSTEM}}\n'],
|
| prompt=['<extra_id_1>User\n{{QUERY}}\n<extra_id_1>Assistant\n'],
|
| chat_sep=['\n']))
|
|
|
|
|
| class MyModelLoader(ModelLoader):
|
|
|
| def get_config(self, model_dir: str) -> PretrainedConfig:
|
| return AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
|
|
|
| def get_processor(self, model_dir: str, config: PretrainedConfig) -> Processor:
|
| return AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
|
|
| def get_model(self, model_dir: str, config: PretrainedConfig, processor: Processor,
|
| model_kwargs) -> PreTrainedModel:
|
| return AutoModelForCausalLM.from_pretrained(
|
| model_dir, config=config, torch_dtype=self.torch_dtype, trust_remote_code=True, **model_kwargs)
|
|
|
|
|
| register_model(
|
| ModelMeta(
|
| model_type='custom',
|
| model_groups=[
|
| ModelGroup([Model('AI-ModelScope/Nemotron-Mini-4B-Instruct', 'nvidia/Nemotron-Mini-4B-Instruct')])
|
| ],
|
| loader=MyModelLoader,
|
| template='custom',
|
| ignore_patterns=['nemo'],
|
| is_multimodal=False,
|
| ))
|
|
|
| if __name__ == '__main__':
|
| infer_request = InferRequest(messages=[{'role': 'user', 'content': 'who are you?'}])
|
| request_config = RequestConfig(max_tokens=512, temperature=0)
|
| engine = TransformersEngine('AI-ModelScope/Nemotron-Mini-4B-Instruct')
|
| response = engine.infer([infer_request], request_config)
|
| swift_response = response[0].choices[0].message.content
|
|
|
| engine.template.template_backend = 'jinja'
|
| response = engine.infer([infer_request], request_config)
|
| jinja_response = response[0].choices[0].message.content
|
| assert swift_response == jinja_response, f'swift_response: {swift_response}\njinja_response: {jinja_response}'
|
| print(f'response: {swift_response}')
|
|
|
