Create app.py

app.py

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+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 = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+DESCRIPTION = """\
+# Llama-2 13B Chat
+This Space demonstrates model [Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat) by Meta, a Llama 2 model with 13B parameters fine-tuned for chat instructions. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints).
+🔎 For more details about the Llama 2 family of models and how to use them with `transformers`, take a look [at our blog post](https://huggingface.co/blog/llama2).
+🔨 Looking for an even more powerful model? Check out the large [**70B** model demo](https://huggingface.co/spaces/ysharma/Explore_llamav2_with_TGI).
+🐇 For a smaller model that you can run on many GPUs, check our [7B model demo](https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat).
+"""
+
+LICENSE = """
+<p/>
+---
+As a derivate work of [Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat) by Meta,
+this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/llama-2-13b-chat/blob/main/USE_POLICY.md).
+"""
+
+if not torch.cuda.is_available():
+ DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+
+
+if torch.cuda.is_available():
+ model_id = "meta-llama/Llama-2-7b-chat-hf"
+ model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
+ tokenizer.use_default_system_prompt = False
+
+
+@spaces.GPU
+def generate(
+ message: str,
+ chat_history: list[tuple[str, str]],
+ system_prompt: str,
+ max_new_tokens: int = 1024,
+ temperature: float = 0.6,
+ top_p: float = 0.9,
+ top_k: int = 50,
+ repetition_penalty: float = 1.2,
+) -> Iterator[str]:
+ conversation = []
+ if system_prompt:
+ conversation.append({"role": "system", "content": system_prompt})
+ for user, assistant in chat_history:
+ conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+ conversation.append({"role": "user", "content": message})
+
+ input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
+ 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,
+ repetition_penalty=repetition_penalty,
+ )
+ 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.Textbox(label="System prompt", lines=6),
+ 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=1.2,
+ ),
+ ],
+ stop_btn=None,
+ examples=[
+ ["Hello there! How are you doing?"],
+ ["Can you explain briefly to me what is the Python programming language?"],
+ ["Explain the plot of Cinderella in a sentence."],
+ ["How many hours does it take a man to eat a Helicopter?"],
+ ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
+ ],
+)
+
+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)
+
+if __name__ == "__main__":
+ demo.queue(max_size=20).launch()