Push model using huggingface_hub.

README.md

@@ -1,81 +1,10 @@
 ---
+pipeline_tag: text-generation
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
 ---
 
 This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
-
----
-
-# GPT-2 Nepali Model
-
-This repository contains a custom GPT-2 model trained on Nepali text. Follow the instructions below to use this model for text generation.
-
----
-
-## How to Use the Model
-
-1. **Download the Required Code**  
-   Save the [`model_code.py`](https://github.com/Aananda-giri/llm.np/blob/main/3.%20GPT-2/sebastian_gutenberg/huggingface_hub/model_code.py) file in the same directory where you'll run the script.
-
-2. **Install Required Libraries**  
-   Ensure you have the necessary libraries installed:  
-   ```bash
-   pip install transformers torch
-   ```
-
-3. **Run the Following Code**  
-   Here's an example to load the model and generate text:
-
-   ```python
-   import torch
-   from model_code import GPTModel, generate_and_print_sample
-   from transformers import PreTrainedTokenizerFast
-
-   # Load the tokenizer
-   tokenizer = PreTrainedTokenizerFast.from_pretrained("Aananda-giri/NepaliBPE")
-
-   # Define the starting text
-   start_context = "रामले भात"
-
-   # Load the pre-trained model
-   loaded_model = GPTModel.from_pretrained("Aananda-giri/GPT2-Nepali")
-
-   # Move the model to the appropriate device (CPU or GPU)
-   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-   loaded_model.to(device)
-
-   # Generate text
-   generate_and_print_sample(
-       loaded_model, tokenizer, device, start_context
-   )
-   ```
-
----
-
-## Additional Notes
-
-- **Tokenizer**: The model uses a pre-trained tokenizer available at `Aananda-giri/NepaliBPE`. Ensure this is downloaded and accessible during runtime.
-- **Dependencies**: This code requires `transformers` (by Hugging Face) and `torch` (PyTorch). Install them if not already installed.
-- **Device Compatibility**: The script automatically detects if a CUDA-enabled GPU is available and utilizes it for faster inference. If not, it defaults to the CPU.
-
----
-
-## Example Output
-
-Input:
-```
-रामले भात
-```
-
-Generated Text:
-```
-रामले भात खाएर सन्तोष माने। ऊ आफ्ना साथीहरूसँग रमाइलो गरिरहेको थियो।
-```
-
----
-
-Let me know if you'd like further assistance!
+- Library: https://huggingface.co/Aananda-giri/GPT2-Nepali/
+- Docs: [More Information Needed]

config.json

@@ -1,6 +1,6 @@
 {
   "cfg": {
-    "context_length": 1024,
+    "context_length": 512,
     "drop_rate": 0.1,
     "emb_dim": 768,
     "n_heads": 12,

model.safetensors

@@ -1,3 +1,3 @@
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