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,
+    LlamaTokenizer,
+)
+
+MAX_MAX_NEW_TOKENS = 1024
+DEFAULT_MAX_NEW_TOKENS = 50
+MAX_INPUT_TOKEN_LENGTH = 512
+
+DESCRIPTION = """\
+# OpenELM-270M-Instruct -- Running on CPU
+This Space demonstrates [apple/OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) by Apple. Please, check the original model card for details.
+For additional detail on the model, including a link to the arXiv paper, refer to the [Hugging Face Paper page for OpenELM](https://huggingface.co/papers/2404.14619) .
+For details on pre-training, instruction tuning, and parameter-efficient finetuning for the model refer to the [OpenELM page in the CoreNet GitHub repository](https://github.com/apple/corenet/tree/main/projects/openelm) .
+"""
+
+LICENSE = """
+<p/>
+---
+As a derivative work of [apple/OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) by Apple,
+this demo is governed by the original [license](https://huggingface.co/apple/OpenELM-270M-Instruct/blob/main/LICENSE)
+Based on the [Norod78/OpenELM_3B_Demo](https://huggingface.co/spaces/Norod78/OpenELM_3B_Demo) space.
+"""
+
+
+model = AutoModelForCausalLM.from_pretrained(
+    "apple/OpenELM-270M-Instruct",
+    revision="eb111ff",    
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "meta-llama/Llama-2-7b-hf",
+    revision="01c7f73",
+    trust_remote_code=True,
+    tokenizer_class=LlamaTokenizer,
+)
+
+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
+
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    max_new_tokens: int = 1024,
+    temperature: float = 0.1,
+    top_p: float = 0.5,
+    top_k: int = 3,
+    repetition_penalty: float = 1.4,
+) -> Iterator[str]:
+    
+    historical_text = ""
+    #Prepend the entire chat history to the message with new lines between each message
+    for user, assistant in chat_history:
+        historical_text += f"\n{user}\n{assistant}"
+        
+    if len(historical_text) > 0:
+        message = historical_text + f"\n{message}"
+    input_ids = tokenizer([message], 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=0.0,
+            maximum=4.0,
+            step=0.1,
+            value=0.1,
+        ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
+            maximum=1.0,
+            step=0.05,
+            value=0.5,
+        ),
+        gr.Slider(
+            label="Top-k",
+            minimum=1,
+            maximum=1000,
+            step=1,
+            value=3,
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1.4,
+        ),
+    ],
+    stop_btn="Stop",
+    cache_examples=False,
+    examples=[
+        ["You are three years old.  Count from one to ten."],
+        ["Explain quantum physics in 5 words or less:"],
+        ["Question: What do you call a bear with no teeth?\nAnswer:"],
+    ],
+)
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    chat_interface.render()
+    gr.Markdown(LICENSE)
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()