### Share a Model: Hugging Face Guide

Sharing your model on the Hugging Face Hub is a great way to contribute to the AI community and save time for others working on similar problems. Here’s how to share your model:

---

#### **Repository Features**
Each model repository on Hugging Face functions like a GitHub repository, supporting versioning, commit history, and visualizing differences. Hugging Face uses **git** and **git-lfs** for version control, so you can manage your models just like you would with a GitHub repo.

You can specify a **revision** parameter to load a particular model version:
```python
model = AutoModel.from_pretrained(
    "julien-c/EsperBERTo-small", revision="4c77982"  # tag, branch, or commit hash
)
```

---

#### **Setup:**
Before uploading, you need to set up your Hugging Face account:

1. **Login through terminal (if using a local setup):**
   ```bash
   huggingface-cli login
   ```
2. **Login through a Jupyter/Colab notebook (if using notebooks):**
   ```bash
   pip install huggingface_hub
   from huggingface_hub import notebook_login
   notebook_login()
   ```
   Follow the link to generate a token.

---

#### **Convert a Model for All Frameworks**

To ensure compatibility across different frameworks (PyTorch, TensorFlow), convert your model into checkpoints for both.

**PyTorch to TensorFlow:**
```python
pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/model", from_tf=True)
pt_model.save_pretrained("path/to/save")
```

**TensorFlow to PyTorch:**
```python
tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/model", from_pt=True)
tf_model.save_pretrained("path/to/save")
```

**JAX/Flax Conversion:**
```python
flax_model = FlaxDistilBertForSequenceClassification.from_pretrained(
    "path/to/model", from_pt=True
)
```

---

#### **Push a Model During Training**

If you want to push a model during training, set the `push_to_hub=True` parameter in your **TrainingArguments**.

For **PyTorch**:
```python
training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True)
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    compute_metrics=compute_metrics,
)
trainer.push_to_hub()
```

For **TensorFlow**, use the `PushToHubCallback`:
```python
from transformers import PushToHubCallback

push_to_hub_callback = PushToHubCallback(
    output_dir="your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model"
)
model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=[push_to_hub_callback])
```

You can also call `push_to_hub()` directly to push models or tokenizers:
```python
pt_model.push_to_hub("my-awesome-model")
```

To push the model under an organization:
```python
pt_model.push_to_hub("my-awesome-org/my-awesome-model")
```

---

#### **Upload with the Web Interface**

If you prefer a no-code approach, visit the [Hugging Face model creation page](https://huggingface.co/new) to:

1. Select the owner (yourself or an organization).
2. Name your model and specify if it’s public or private.
3. Choose a license.

Once the repo is created, go to the **Files** tab, click **Add file**, and drag-and-drop your model files. Don't forget to add a commit message.

---

#### **Add a Model Card**

A model card is essential for communicating your model’s strengths, limitations, biases, and ethical considerations. You can create it by:

1. Manually adding a **README.md** file.
2. Clicking **Edit model card** in your repository.

For detailed guidelines on writing a model card, check the Hugging Face documentation or look at examples like the **DistilBert** card.

---
