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update doc: add builtin models using guide
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Signed-off-by: mgqa34 <[email protected]>
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mgqa34 committed Jun 21, 2023
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3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -24,4 +24,5 @@ Use [FATE-LLM deployment packages](https://github.com/FederatedAI/FATE/wiki/Down

## Quick Start
- [Federated ChatGLM-6B Training](./doc/tutorial/ChatGLM-6B_ds.ipynb)
- [GPT-2 Training](./doc/tutorial/GPT2-example.ipynb)
- [GPT-2 Training](./doc/tutorial/GPT2-example.ipynb)
- [Builtin Models](./doc/tutorial/builtin_models.md)
18 changes: 18 additions & 0 deletions doc/tutorial/builtin_models.md
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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.



| 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 |

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