File size: 2,486 Bytes
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"""
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 typing import Any, Dict
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
from swift.llm import (InferRequest, Model, ModelGroup, ModelInfo, ModelMeta, PtEngine, RequestConfig, TemplateMeta,
register_model, register_template)
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']))
def get_function(model_dir: str,
model_info: ModelInfo,
model_kwargs: Dict[str, Any],
load_model: bool = True,
**kwargs):
# ref: https://github.com/modelscope/ms-swift/blob/main/swift/llm/model/register.py#L182
model_config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
tokenizer.pad_token_id = tokenizer.eos_token_id
model = None
if load_model:
model = AutoModelForCausalLM.from_pretrained(
model_dir, config=model_config, torch_dtype=model_info.torch_dtype, trust_remote_code=True, **model_kwargs)
return model, tokenizer
register_model(
ModelMeta(
model_type='custom',
model_groups=[
ModelGroup([Model('AI-ModelScope/Nemotron-Mini-4B-Instruct', 'nvidia/Nemotron-Mini-4B-Instruct')])
],
template='custom',
get_function=get_function,
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 = PtEngine('AI-ModelScope/Nemotron-Mini-4B-Instruct')
response = engine.infer([infer_request], request_config)
swift_response = response[0].choices[0].message.content
engine.default_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}')
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