chore: import upstream snapshot with attribution

2026-07-13 13:34:58 +08:00
commit a203934033
1368 changed files with 175001 additions and 0 deletions
@@ -0,0 +1,59 @@
+# Copyright (c) ModelScope Contributors. All rights reserved.
+"""
+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}')