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import os |
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from threading import Thread |
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from typing import Iterator |
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import gradio as gr |
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import spaces |
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import torch |
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from transformers import ( |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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TextIteratorStreamer, |
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LlamaTokenizer, |
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) |
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MAX_MAX_NEW_TOKENS = 1024 |
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DEFAULT_MAX_NEW_TOKENS = 50 |
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MAX_INPUT_TOKEN_LENGTH = 512 |
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DESCRIPTION = """\ |
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# Phi-3-mini-4k-instruct |
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This Space demonstrates [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) by Microsoft. Please, check the original model card for details. |
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For additional detail on the model, including a link to the arXiv paper, refer to the [Hugging Face Paper page for Phi 3](https://huggingface.co/papers/2404.14219) . |
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""" |
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model = AutoModelForCausalLM.from_pretrained( |
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"microsoft/Phi-3-mini-4k-instruct", |
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trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct") |
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if tokenizer.pad_token == None: |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.pad_token_id = tokenizer.eos_token_id |
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model.config.pad_token_id = tokenizer.eos_token_id |
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def generate( |
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message: str, |
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chat_history: list[tuple[str, str]], |
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max_new_tokens: int = 1024, |
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temperature: float = 0.1, |
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top_p: float = 0.4, |
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top_k: int = 10, |
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repetition_penalty: float = 1.4, |
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) -> Iterator[str]: |
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historical_text = "" |
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for user, assistant in chat_history: |
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historical_text += f"\n{user}\n{assistant}" |
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if len(historical_text) > 0: |
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message = historical_text + f"\n{message}" |
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input_ids = tokenizer([message], return_tensors="pt").input_ids |
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if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: |
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input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] |
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gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") |
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input_ids = input_ids.to(model.device) |
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streamer = TextIteratorStreamer(tokenizer, timeout=30.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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{"input_ids": input_ids}, |
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streamer=streamer, |
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max_new_tokens=max_new_tokens, |
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do_sample=True, |
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top_p=top_p, |
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top_k=top_k, |
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temperature=temperature, |
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num_beams=1, |
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pad_token_id = tokenizer.eos_token_id, |
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repetition_penalty=repetition_penalty, |
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no_repeat_ngram_size=5, |
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early_stopping=False, |
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) |
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t = Thread(target=model.generate, kwargs=generate_kwargs) |
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t.start() |
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outputs = [] |
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for text in streamer: |
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outputs.append(text) |
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yield "".join(outputs) |
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chat_interface = gr.ChatInterface( |
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fn=generate, |
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additional_inputs=[ |
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gr.Slider( |
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label="Max new tokens", |
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minimum=1, |
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maximum=MAX_MAX_NEW_TOKENS, |
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step=1, |
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value=DEFAULT_MAX_NEW_TOKENS, |
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), |
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gr.Slider( |
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label="Temperature", |
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minimum=0.1, |
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maximum=4.0, |
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step=0.1, |
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value=0.1, |
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), |
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gr.Slider( |
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label="Top-p (nucleus sampling)", |
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minimum=0.05, |
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maximum=1.0, |
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step=0.05, |
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value=0.5, |
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), |
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gr.Slider( |
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label="Top-k", |
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minimum=1, |
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maximum=1000, |
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step=1, |
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value=3, |
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), |
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gr.Slider( |
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label="Repetition penalty", |
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minimum=1.0, |
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maximum=2.0, |
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step=0.05, |
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value=1.4, |
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), |
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], |
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stop_btn=None, |
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cache_examples=False, |
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examples=[ |
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["Explain quantum physics in 5 words or less:"], |
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["Question: What do you call a bear with no teeth?\nAnswer:"], |
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], |
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) |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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chat_interface.render() |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
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