final_update

app.py

@@ -1,64 +1,151 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+ # import gradio as gr
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer, pipeline
+from threading import Thread
+
+model_id = "rasyosef/Llama-3.2-180M-Amharic-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+llama_am = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    pad_token_id=tokenizer.pad_token_id,
+    eos_token_id=tokenizer.eos_token_id
+  )
+
+# Function that accepts a prompt and generates text using the phi2 pipeline
+def generate(message, chat_history, max_new_tokens=64):
+
+  history = []
+
+  for sent, received in chat_history:
+    history.append({"role": "user", "content": sent})
+    history.append({"role": "assistant", "content": received})
+
+  history.append({"role": "user", "content": message})
+  #print(history)
+
+  if len(tokenizer.apply_chat_template(history)) > 512:
+    yield "chat history is too long"
+  else:
+    # Streamer
+    streamer = TextIteratorStreamer(tokenizer=tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=300.0)
+    thread = Thread(target=llama_am,
+                    kwargs={
+                        "text_inputs":history,
+                        "max_new_tokens":max_new_tokens,
+                        "repetition_penalty":1.15,
+                        "streamer":streamer
+                        }
+                    )
+    thread.start()
+
+    generated_text = ""
+    for word in streamer:
+      generated_text += word
+      response = generated_text.strip()
+
+      yield response
+
+# Chat interface with gradio
+with gr.Blocks() as demo:
+  gr.Markdown("""
+  # Llama 3.2 180M Amharic Chatbot Demo
+
+  This chatbot was created using [Llama-3.2-180M-Amharic-Instruct](https://huggingface.co/rasyosef/Llama-3.2-180M-Amharic-Instruct), a finetuned version of my 180 million parameter [Llama 3.2 180M Amharic](https://huggingface.co/rasyosef/Llama-3.2-180M-Amharic) transformer model.
+  """)
+
+  tokens_slider = gr.Slider(8, 256, value=64, label="Maximum new tokens", info="A larger `max_new_tokens` parameter value gives you longer text responses but at the cost of a slower response time.")
+
+  chatbot = gr.ChatInterface(
+    chatbot=gr.Chatbot(height=400),
+    fn=generate,
+    additional_inputs=[tokens_slider],
+    stop_btn=None,
+    cache_examples=False,
+    examples=[
+        ["ሰላም፣ እንዴት ነህ?"],
+        ["የኢትዮጵያ ዋና ከተማ ስም ምንድን ነው?"],
+        ["የኢትዮጵያ የመጨረሻው ንጉስ ማን ነበሩ?"],
+        ["የአማርኛ ግጥም ፃፍልኝ"],
+        ["ተረት ንገረኝ\n\nጅብና አንበሳ"],
+        ["አንድ አስቂኝ ቀልድ ንገረኝ"],
+        ["የተሰጠው ጽሑፍ አስተያየት ምን አይነት ነው? 'አዎንታዊ'፣ 'አሉታዊ' ወይም 'ገለልተኛ' የሚል ምላሽ ስጥ። 'አሪፍ ፊልም ነበር'"],
+        ["የፈረንሳይ ዋና ከተማ ስም ምንድን ነው?"],
+        ["አሁን የአሜሪካ ፕሬዚዳንት ማን ነው?"],
+        ["ሶስት የአፍሪካ ሀገራት ጥቀስልኝ"],
+        ["3 የአሜሪካ መሪዎችን ስም ጥቀስ"],
+        ["5 የአሜሪካ ከተማዎችን ጥቀስ"],
+        ["አምስት የአውሮፓ ሀገሮችን ጥቀስልኝ"],
+        ["በ ዓለም ላይ ያሉትን 7 አህጉራት ንገረኝ"]
+      ]
+  )
+
+demo.queue().launch(debug=True,share=True)
+# from huggingface_hub import InferenceClient
+
+# """
+# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+# """
+# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+
+# def respond(
+#     message,
+#     history: list[tuple[str, str]],
+#     system_message,
+#     max_tokens,
+#     temperature,
+#     top_p,
+# ):
+#     messages = [{"role": "system", "content": system_message}]
+
+#     for val in history:
+#         if val[0]:
+#             messages.append({"role": "user", "content": val[0]})
+#         if val[1]:
+#             messages.append({"role": "assistant", "content": val[1]})
+
+#     messages.append({"role": "user", "content": message})
+
+#     response = ""
+
+#     for message in client.chat_completion(
+#         messages,
+#         max_tokens=max_tokens,
+#         stream=True,
+#         temperature=temperature,
+#         top_p=top_p,
+#     ):
+#         token = message.choices[0].delta.content
+
+#         response += token
+#         yield response
+
+
+# """
+# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+# """
+# demo = gr.ChatInterface(
+#     respond,
+#     additional_inputs=[
+#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#         gr.Slider(
+#             minimum=0.1,
+#             maximum=1.0,
+#             value=0.95,
+#             step=0.05,
+#             label="Top-p (nucleus sampling)",
+#         ),
+#     ],
+# )
+
+
+# if __name__ == "__main__":
+#     demo.launch()