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Merge pull request #16 from FederatedAI/feature-1.2.0-doc_builtin_models
update doc: add builtin models using guide
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## Builtin Models | ||
FATE-LLM provide some builtin models, users can use them simply to efficiently train their language models. | ||
To use these models, please read the using tutorial of [ChatGLM-6B Training Guide](./ChatGLM-6B_ds.ipynb) and [GPT2 Training Guide](GPT2-example.ipynb). | ||
After reading the training tutorial above, it's easy to use other models listing in the following tabular by changing `module_name`, `class_name`, `dataset` to `ModuleName`, `ClassName`, `DatasetName` respectively list below. | ||
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| Model | ModuleName | ClassName | DataSetName | | ||
| -------------- | ----------------- | --------------------------------- | ---------------- | | ||
| LLaMA-7B | pellm.llama | LLAMAForCausalLM | llama_tokenizer | | ||
| ChatGLM-6B | pellm.chatglm | ChatGLMForConditionalGeneration | glm_tokenizer | | ||
| GPT-2 | pellm.gpt2 | GPT2 | nlp_tokenizer | | ||
| ALBERT | pellm.albert | Albert | nlp_tokenizer | | ||
| BART | pellm.bart | Bart | nlp_tokenizer | | ||
| BERT | pellm.bert | Bert | nlp_tokenizer | | ||
| DeBERTa | pellm.deberta | Deberta | nlp_tokenizer | | ||
| DistilBERT | pellm.distilbert | DistilBert | nlp_tokenizer | | ||
| RoBERTa | pellm.roberta | Roberta | nlp_tokenizer | |