feat: add llama template.

app.py

@@ -1,63 +1,143 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import os
+import spaces
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from threading import Thread
+from typing import Generator
 
+# Set an environment variable
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+
+DESCRIPTION = """
+<div>
+<h1 style="text-align: center;">SPUM Table Extraction</h1>
+<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/khulaifi95/Llama-3.1-8B-Reason-Blend-888k"><b>Meta Llama3 8b Chat</b></a>. Meta Llama3 is the new open LLM and comes in two sizes: 8b and 70b. Feel free to play with it, or duplicate to run privately!</p>
+</div>
 """
-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
+
+PLACEHOLDER = """
+<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
+   <img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/8e75e61cc9bab22b7ce3dec85ab0e6db1da5d107/Meta_lockup_positive%20primary_RGB.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55;  "> 
+   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Materials GPT</h1>
+   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
+</div>
+"""
+
+
+css = """
+h1 {
+  text-align: center;
+  display: block;
+}
+#duplicate-button {
+  margin: auto;
+  color: white;
+  background: #1565c0;
+  border-radius: 100vh;
+}
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# Load the tokenizer and model
+model_id = "khulaifi95/Llama-3.1-8B-Reason-Blend-888k"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
 
-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]})
+@spaces.GPU(duration=120)
+def chat_llama3_8b(
+    message: str, history: list, temperature: float, max_new_tokens: int
+) -> Generator[str, None, None]:
+    """
+    Generate a streaming response using the llama3-8b model.
+    Args:
+        message (str): The input message.
+        history (list): The conversation history used by ChatInterface.
+        temperature (float): The temperature for generating the response.
+        max_new_tokens (int): The maximum number of new tokens to generate.
+    Returns:
+        str: The generated response.
+    """
+    conversation = []
+    for user, assistant in history:
+        conversation.extend(
+            [
+                {"role": "user", "content": user},
+                {"role": "assistant", "content": assistant},
+            ]
+        )
+    conversation.append({"role": "user", "content": message})
 
-    messages.append({"role": "user", "content": message})
+    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(
+        model.device
+    )
 
-    response = ""
+    streamer = TextIteratorStreamer(
+        tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
+    )
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
+    generate_kwargs = dict(
+        input_ids=input_ids,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
         temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+        eos_token_id=terminators,
+    )
+    # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.
+    if temperature == 0:
+        generate_kwargs["do_sample"] = False
 
-        response += token
-        yield response
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
 
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        # print(outputs)
+        yield "".join(outputs)
 
-"""
-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)",
+
+# Gradio block
+chatbot = gr.Chatbot(height=450, placeholder=PLACEHOLDER, label="Gradio ChatInterface")
+
+with gr.Blocks(fill_height=True, css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.ChatInterface(
+        fn=chat_llama3_8b,
+        chatbot=chatbot,
+        fill_height=True,
+        additional_inputs_accordion=gr.Accordion(
+            label="⚙️ Parameters", open=False, render=False
         ),
-    ],
-)
+        additional_inputs=[
+            gr.Slider(
+                minimum=0,
+                maximum=1,
+                step=0.1,
+                value=0.95,
+                label="Temperature",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=128,
+                maximum=4096,
+                step=1,
+                value=512,
+                label="Max new tokens",
+                render=False,
+            ),
+        ],
+        examples=[
+            ["How to setup a human base on Mars? Give short answer."],
+            ["Explain theory of relativity to me like I’m 8 years old."],
+            ["What is 9,000 * 9,000?"],
+            ["The detonative temperature of this polypropylene is 2000°F."],
+            ["The preparation method according to claim 1, characterized in that the SO2 accounts for 30 wt% and the Fe2O3 accounts for 70 wt%."],
+        ],
+        cache_examples=False,
+    )
 
 
 if __name__ == "__main__":