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docs/TUTORIAL_3_MODEL_eng.md

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+# Base class
+
+The base class BaseModel implements common methods for loading/saving models from a local file or directory or from a library-provided pretrained model configuration (downloaded from BAAI modelhub's Kingsoft S3 repository).
+All supported models now support the three most common model types [encoder, decoder and encoder-decoder]. GLM models can now load all GLM series models, see https://github.com/THUDM/GLM
+
+## From_pretrain
+
+Models with the same model structure can be loaded with the same class. For example, BERT-base and Roberta-base models can be loaded with the BertModel class. From_pretrain is optimized for data/model parallel model loading to avoid resource waste caused by repeated downloads.
+By calling ClassName.from_pretrian() to load, now our model hub supports the following models, you can directly download the model configuration file [config.json], model weights [pytorch_model.bin], and dictionary files [vocab .txt]. example:
+
+````python
+from flagai.model.glm_model import GLMForSingleTokenCloze
+model = GLMForSingleTokenCloze.from_pretrain(download_path="./state_dict", model_name="GLM-large-ch")
+````
+
+If the model weights are loaded locally, they can also be loaded through ClassName.from_pretrain(). example:
+Load the model file `pytorch_model.bin` from the `./state_dict/GLM-large-ch` directory
+
+````python
+from flagai.model.glm_model import GLMForSingleTokenCloze
+model = GLMForSingleTokenCloze.from_pretrain(download_path="./state_dict",
+                                               model_name="GLM-large-ch")
+````
+
+## All supported models
+
+| ClassName | ModelName | Language | Model Type |
+|-----------------------------------|------------- ----|----------|------------|
+| flagai.model.glm_model.GLMModel | GLM-10b-ch | chinese | encoder |
+| flagai.model.glm_model.GLMModel | GLM-large-ch | chinese | encoder |
+| flagai.model.bert_model.BertModel | RoBERTa-base-ch | chinese | encoder |
+| flagai.model.gpt2_model.GPT2Model | GPT2_base_ch | chinese | decoder |
+| flagai.model.t5_model.T5Model | T5-base-ch | chinese | enc2dec |
+| flagai.model.t5_model.T5Model | T5-base-en | chinese | enc2dec |
+| flagai.model.bert_model.BertModel | BERT-base-en | english | encoder |
+| flagai.model.glm_model.GLMModel | GLM-large-en | english | encoder |
+
+## Supported models + tasks
+
+At the same time, we support the finetuned model on the task, as shown in the table below, the model weights can be loaded through ClassName.from_pretrain(), for example, we automatically download and load a GLM trained on the title-generation task -large-ch model:
+
+````python
+from flagai.model.glm_model import GLMForSeq2Seq
+model = GLMForSeq2Seq.from_pretrain(model_name='GLM-large-ch')
+````
+
+We also provide the AutoLoader class to help load models. For example, the GLM-large-ch model is used for seq2seq tasks. Here we adopt a task- and model-independent design. In theory, tasks and models can be freely replaced.
+
+````python
+from flagai.auto_model.auto_loader import AutoLoader
+auto_loader = AutoLoader("seq2seq",
+                         model_name="GLM-large-ch",
+                         model_dir= "./state_dict")
+model = auto_loader.get_model()
+````
+
+| ClassName | Model Name | language | Task |
+|------------------------------------------------- |-----------------|----------|-------------------|
+| flagai.model.glm_model.GLMForSeq2Seq | GLM-large-ch | chinese | title generation |
+| flagai.model.glm_model.GLMForSeq2Seq | GLM-large-ch | chinese | poetry generation |
+| flagai.model.bert_model.BertForSequenceLabeling | RoBERTa-base-ch | chinese | title generation |
+| flagai.model.bert_model.BertForSequenceLabeling | RoBERTa-base-ch | chinese | NER |
+| flagai.model.bert_model.BertForSequenceLabeling | RoBERTa-base-ch | chinese | semantic matching |
+| flagai.model.t5_model.T5Model | T5-base-ch | chinese | title generation |
+| flagai.model.bert_model.BertForSequenceLabeling | BERT-base-en | english | title gneration |
+
+## Model design
+
+The main construction logic of the model `layer->block>model`
+`flagai.model.layer`: including mlp, layernorm, activation, attention and other layers
+
+`flagai.model.block`: Build a transformer block by assembling various layers, such as BERT block, etc.
+
+`flagai.model`: build the model by embedding layers and stacked blocks
+
+## forward function
+
+Model's forward function:
+Input is keyword arguments: including input_ids, position_ids, attention_mask, etc., redundant parameters will be automatically ignored
+For example, GLM's forward function:
+
+````python
+def forward(self,
+            input_ids=None,
+            position_ids=None,
+            attention_mask=None,
+            mems=None,
+            return_memory=False,
+            detach_memory=True,
+            prompt_pos=None,
+            **kwargs)
+````
+
+The output is a dictionary, including logits and hidden states, which are required, such as the return of the GLM forword function:
+
+````python
+return {'loss': loss, 'logits': logits, 'hidden_states': mems}
+````
+
+## init_from_json
+Model's init_from json function:
+The input is a dictionary, the output is an initialized model
+For example, the invocation of GLMModel is as follows:
+
+````python
+GLMModel.init_from_json(config_file = "./config.json", **kwargs)
+````
+
+**kwargs are reserved parameters, in order to be compatible with new initialization parameters of some models