# Modelers Contribution Guidelines
[](https://gitee.com/mindspore/docs/blob/r2.4.0/docs/mindformers/docs/source_en/faq/modelers_contribution.md)
## Upload a Model to the Modelers Community
Modelers Community is a model hosting platform where users can upload custom models to [Magic Music Community](https://modelers.cn/) for hosting.
### MindFormers Built-in Models
If the custom model uses a built-in model provided by MindFormers, i.e. a model whose model code is located under mindformers/models, and no modifications have been made to the model's structure code. You only need to upload the weight file and configuration.
For example, if a user uses MindFormers built-in ChatGLM2 model, performs fine-tuning training, and wants to share the fine-tuned model weights, uploading the model configuration and weights file is sufficient.
Below is sample code that saves the model configuration and weights:
```python
import mindspore as ms
from mindformers import ChatGLM2Config, ChatGLM2ForConditionalGeneration
config = ChatGLM2Config()
model = ChatGLM2ForConditionalGeneration(config)
ms.load_checkpoint("path/model.ckpt", model) # Load custom weights
model.save_pretrained("./my_model", save_json=True)
```
The above code runs and saves the config.json file and the mindspore_model.ckpt file (larger weights are automatically split and saved).
After saving, you can use the openmind_hub library for model uploading. See [Model Upload](https://modelers.cn/docs/zh/best-practices/community_contribution/model_contribution.html#%E4%BD%BF%E7%94%A8openmind-hub-client%E4%B8%8A%E4%BC%A0%E6%A8%A1%E5%9E%8B).
```python
import openmind_hub
openmind_hub.upload_folder(
folder_path="/path/to/local/folder",
repo_id="username/your-model-name",
token="your-token",
)
```
An uploaded example can be found in the [OpenLlama model](https://modelers.cn/models/MindSpore-Lab/llama_7b/tree/main) of the Modelers community.
### Custom Models
If the user has customized model code, you need to upload the model code file at the same time and add a mapping in the json configuration file so that it can be imported through the Auto class.
#### Naming Rules
Custom code files uploaded to the community generally have uniform naming rules. Assuming the custom model is named model, its code naming should be as follows:
```text
---- model
|- configuration_model.py # Config class code files
|- modeling_model.py # Model class code files
|- tokenization_model.py # Tokenizer code files
```
#### Adding auto Mapping
In order for the Auto class to be able to find the user-defined model class when it is used, you need to add the auto mapping in the config.json file. The contents of the additions are as follows:
```json
{
"auto_map": {
"AutoConfig": "configuration_model.MyConfig",
"AutoModel": "modeling_model.MyModel",
"AutoModelForCausalLM": "modeling_model.MyModelForCausalLM",
},
}
```
If there is a custom tokenizer, the tokenizer needs to be saved:
```python
tokenizer.save_pretrained("./my_model", save_json=True)
```
And add auto mapping to the saved tokenizer_config.json:.
```json
{
"auto_map": {
"AutoTokenizer": ["tokenization_model.MyTokenizer", "tokenization_model.MyFastTokenizer"]
},
}
```
#### Uploading the Model
Model uploading can be done using the openmind_hub library. See [Model Upload](https://modelers.cn/docs/zh/best-practices/community_contribution/model_contribution.html#%E4%BD%BF%E7%94%A8openmind-hub-client%E4%B8%8A%E4%BC%A0%E6%A8%A1%E5%9E%8B).
```python
import openmind_hub
openmind_hub.upload_folder(
folder_path="/path/to/local/folder",
repo_id="username/your-model-name",
token="your-token",
)
```
The uploaded example can be found in the [Model](https://modelers.cn/models/MindSpore-Lab/internlm2-7b/tree/main) of the Modelers community.