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app.py

@@ -8,7 +8,7 @@ import os
 sys.path.append(os.path.join(os.path.dirname(__file__), "model"))
 from modeling_ngwanda import NgwandaModel
 
-# Load the wrapped tokenizer from sentencepiece
+# Load the wrapped tokenizer
 tokenizer = LlamaTokenizer.from_pretrained("./ngwanda-tokenizer-hf")
 # Load your Ngwanda base model
 model = NgwandaModel.from_pretrained("./model")
@@ -35,8 +35,8 @@ def predict(text, tokens, temp):
 # Gradio Interface (Text Completion style)
 gr.Interface(
     fn=predict,
-    inputs=[gr.Textbox(label="Shona Text"), gr.Slider(10, 200, value=50, step=1, label="Max New Tokens"), gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature")],
+    inputs=[gr.Textbox(label="Shona Input"), gr.Slider(10, 200, value=50, step=1, label="Max New Tokens"), gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature")],
     outputs="text",
-    title="Shona Transformer Base Model",
+    title="Ngwanda Base Model",
     description="A base language model for Shona."
 ).launch()

model/modeling_ngwanda.py

@@ -18,6 +18,7 @@ class NgwandaModel(PreTrainedModel):
             V=config.vocab_size,
             layers=config.layers
         )
+        self.post_init()
         
     def forward(self, input_ids, **kwargs):
         logits, _ = self.transformer(input_ids)