Create app.py

app.py

@@ -0,0 +1,53 @@
+import os
+
+hf_token= os.getenv("access_token")
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("afrizalha/Sasando-1-25M", token=hf_token)
+tiny = AutoModelForCausalLM.from_pretrained("afrizalha/Sasando-1-25M", token=hf_token)
+tinier = AutoModelForCausalLM.from_pretrained("afrizalha/Sasando-1-7M", token=hf_token)
+
+
+desc = """Sasando-1 is a tiny, highly experimental text generator built using the Phi-3 architecture. It comes with two variations of microscopic sizes: 7M and 25M parameters. It is trained on a tightly-controlled Indo4B dataset filtered to only have 18000 unique words. The method is inspired by Microsoft's TinyStories paper which demonstrates that a tiny language model can produce fluent text when trained on tightly-controlled dataset."""
+
+def generate(starting_text, choice, num_runs,temp,top_p):
+    if choice == '7M': 
+        model = tinier
+    elif choice == '25M': 
+        model = tiny
+    elif choice == 'Info':
+        return desc
+        
+    results = []
+    for i in range(num_runs):
+        inputs = tokenizer([starting_text], return_tensors="pt").to(model.device)
+        outputs = model.generate(
+            inputs=inputs.input_ids,
+            max_new_tokens=32-len(inputs.input_ids),
+            do_sample=True,        
+            temperature=temp,
+            top_p=top_p
+        )
+        outputs = tokenizer.batch_decode(outputs,skip_special_tokens=True)[0]
+        outputs = outputs[:outputs.find(".")]
+        results.append(outputs)
+        yield "\n\n".join(results)
+
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+    starting_text = gr.Textbox(label="Starting text", value="cinta adalah")
+    choice = gr.Radio(["7M", "25M"], label="Model size", info="Built with the Phi-3 architecture")
+    num_runs = gr.Slider(label="Number of examples", minimum=1, maximum=10, step=1, value=5)
+    temp = gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, step=0.1, value=0.7)
+    top_p = gr.Slider(label="Top P", minimum=0.1, maximum=1.0, step=0.1, value=0.5)
+    res = gr.Textbox(label="Continuation")
+    gr.Interface(
+        fn=generate,
+        inputs=[starting_text,choice,num_runs,temp,top_p],
+        outputs=[res],
+        allow_flagging="never",
+        title="Sasando-1",
+        )
+    examples=gr.Examples([["gue"], ["presiden"], ["cinta adalah"], ["allah, aku"]], [starting_text])
+
+app.launch(share=True)