| |
| """ |
| 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}') |
|
|
