Update app.py

app.py

@@ -1,3 +1,175 @@
+import os
+from threading import Thread
+from typing import Iterator
+
 import gradio as gr
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+MAX_MAX_NEW_TOKENS = 1024
+DEFAULT_MAX_NEW_TOKENS = 256
+MAX_INPUT_TOKEN_LENGTH = 512
+
+DESCRIPTION = """\
+# Buzz-3B-Small
+This Space demonstrates Buzz-3b-small-v0.6.3.
+"""
+
+LICENSE = """
+<p/>
+---
+This demo uses Buzz-3b-small-v0.6.3. Please check the model card for details.
+"""
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo works better on GPU.</p>"
+
+model_id = "H-D-T/Buzz-3b-small-v0.6.3"
+
+if torch.cuda.is_available():
+    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, low_cpu_mem_usage=True)
+else:
+    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cpu", trust_remote_code=True, low_cpu_mem_usage=True)
+    
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+if tokenizer.pad_token == None:
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+    model.config.pad_token_id = tokenizer.eos_token_id
+
+# Define the special tokens
+bos_token = "<|begin_of_text|>"
+eos_token = "<|eot_id|>"
+start_header_id = "<|start_header_id|>"
+end_header_id = "<|end_header_id|>"
+
+def format_chat_history(chat_history: list[tuple[str, str]], add_generation_prompt=False) -> str:
+    """
+    Formats the chat history according to the model's chat template.
+    """
+    chat_template = f"""
+    {{% if not add_generation_prompt is defined %}}{{% set add_generation_prompt = false %}}{{% endif %}}
+    {{% set loop_messages = messages %}}
+    {{% for message in loop_messages %}}
+        {{% set content = '{start_header_id}' + message['role'] + '{end_header_id}\\n\\n' + message['content'].strip() + '{eos_token}' %}}
+        {{% if loop.index0 == 0 %}}{{% set content = bos_token + content %}}{{% endif %}}
+        {{ content }}
+    {{% endfor %}}
+    {{% if add_generation_prompt %}}{{ '{start_header_id}assistant{end_header_id}\\n\\n' }}{{% else %}}{{ eos_token }}{{% endif %}}
+    """
+    chat_context = ""
+    for i, (user, assistant) in enumerate(chat_history):
+        user_msg = start_header_id + "user" + end_header_id + "\n\n" + user.strip() + eos_token
+        assistant_msg = start_header_id + "assistant" + end_header_id + "\n\n" + assistant.strip() + eos_token
+        if i == 0:
+            user_msg = bos_token + user_msg
+        chat_context += user_msg + assistant_msg
+
+    if add_generation_prompt:
+        chat_context += start_header_id + "assistant" + end_header_id + "\n\n"
+    else:
+        chat_context += eos_token
+
+    return chat_context
+
+@spaces.GPU
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    max_new_tokens: int = 1024,
+    temperature: float = 0.6,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.4,
+) -> Iterator[str]:
+    
+    chat_history.append(("user", message))
+    chat_context = format_chat_history(chat_history, add_generation_prompt=True)
+    input_ids = tokenizer([chat_context], return_tensors="pt").input_ids
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        {"input_ids": input_ids},
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        pad_token_id = tokenizer.eos_token_id,
+        repetition_penalty=repetition_penalty,
+        no_repeat_ngram_size=5,
+        early_stopping=False,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
+    additional_inputs=[
+        gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Slider(
+            label="Temperature",
+            minimum=0.1,
+            maximum=4.0,
+            step=0.1,
+            value=0.6,
+        ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
+            maximum=1.0,
+            step=0.05,
+            value=0.9,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=50,
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1.4,
+        ),
+    ],
+    stop_btn=None,
+    examples=[
+        ["A recipe for a chocolate cake:"],
+        ["Can you explain briefly to me what is the Python programming language?"],
+        ["Explain the plot of Cinderella in a sentence."],
+        ["Question: What is the capital of France?\nAnswer:"],
+        ["Question: I am very tired, what should I do?\nAnswer:"],
+    ],
+)
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+    chat_interface.render()
+    gr.Markdown(LICENSE)
 
-gr.load("models/H-D-T/Buzz-3b-small-v0.6.3").launch()
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()