Create app.py

app.py

@@ -0,0 +1,74 @@
+import gradio as gr
+from threading import Thread
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, TextIteratorStreamer
+
+model_id = "rasyosef/llama-3.2-amharic-28k-512"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+def generate(prompt):
+  prompt_length = len(tokenizer.tokenize(prompt))
+  if prompt_length >= 128:
+    yield prompt + "\n\nPrompt is too long. It needs to be less than 128 tokens."
+  else:
+    inputs = tokenizer(prompt, return_tensors="pt")
+    inputs["input_ids"][0][0] = 0
+    print(inputs)
+
+    max_new_tokens = max(0, 128 - prompt_length)
+    streamer = TextIteratorStreamer(tokenizer=tokenizer, skip_prompt=False, skip_special_tokens=True, timeout=300.0)
+    thread = Thread(
+      target=model.generate,
+      kwargs={
+        "inputs": inputs["input_ids"],
+        "attention_mask": inputs["attention_mask"],
+        "max_new_tokens": max_new_tokens,
+        "temperature": 0.4,
+        "do_sample": True,
+        "top_k": 8,
+        "top_p": 0.8,
+        "repetition_penalty": 1.4,
+        "streamer": streamer,
+        "pad_token_id": tokenizer.pad_token_id,
+        "eos_token_id": tokenizer.eos_token_id
+      })
+    thread.start()
+
+    generated_text = ""
+    for word in streamer:
+      generated_text += word
+      response = generated_text.strip()
+      yield response
+
+with gr.Blocks(css="#prompt_textbox textarea {color: blue}") as demo:
+  gr.Markdown("""
+  # Llama 3.2 Amharic
+  This is a demo for [llama-3.2-amharic](https://huggingface.co/rasyosef/llama-3.2-amharic-28k-512), a smaller version of Meta's [Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) decoder transformer model pretrained for 1.5 days on `276 million` tokens of **Amharic** text. This model has `155 million` parameters and a context size of `512` tokens. This is a base model and hasn't undergone any supervised finetuing yet.
+  
+  Please **enter a prompt** and click the **Generate** button to generate completions for the prompt.
+  #### Text generation parameters:
+  - `temperature` : **0.4**
+  - `do_sample` : **True**
+  - `top_k` : **8**
+  - `top_p` : **0.8**
+  - `repetition_penalty` : **1.4**
+  """)
+
+  prompt = gr.Textbox(label="Prompt", placeholder="Enter prompt here", lines=4, interactive=True, elem_id="prompt_textbox")
+  with gr.Row():
+    with gr.Column():
+      gen = gr.Button("Generate")
+    with gr.Column():
+      btn = gr.ClearButton([prompt])
+  gen.click(generate, inputs=[prompt], outputs=[prompt])
+  examples = gr.Examples(
+        examples=[
+            "አዲስ አበባ",
+            "በእንግሊዙ ፕሬሚየር ሊግ",
+            "ፕሬዚዳንት ዶናልድ ትራምፕ",
+            "በመስቀል አደባባይ"
+          ],
+        inputs=[prompt],
+    )
+demo.queue().launch(debug=True)