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import torch |
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from PIL import Image |
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import gradio as gr |
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import spaces |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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import os |
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from threading import Thread |
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HF_TOKEN = os.environ.get("HF_TOKEN", None) |
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MODEL_ID = "elyza/Llama-3-ELYZA-JP-8B" |
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MODELS = os.environ.get("MODELS") |
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MODEL_NAME = MODELS.split("/")[-1] |
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TITLE = "<h1><center>Llama-3-ELYZA-JP-8B Chat webui</center></h1>" |
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DESCRIPTION = f""" |
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<h3>MODEL: <a href="https://hf.co/{MODELS}">{MODEL_NAME}</a></h3> |
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<center> |
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<p>youko-8b is the large language model built by rinna. |
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<br> |
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Feel free to test without log. |
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</p> |
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</center> |
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""" |
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model = AutoModelForCausalLM.from_pretrained( |
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MODELS, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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tokenizer = AutoTokenizer.from_pretrained(MODELS) |
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@spaces.GPU |
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def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float): |
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print(f'message is - {message}') |
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print(f'history is - {history}') |
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conversation = [] |
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for prompt, answer in history: |
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conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}]) |
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conversation.append({"role": "user", "content": message}) |
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input_ids = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer(input_ids, return_tensors="pt").to(0) |
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streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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inputs, |
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streamer=streamer, |
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top_k=top_k, |
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top_p=top_p, |
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repetition_penalty=penalty, |
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max_new_tokens=max_new_tokens, |
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do_sample=True, |
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temperature=temperature, |
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eos_token_id = [128001, 128009], |
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) |
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thread = Thread(target=model.generate, kwargs=generate_kwargs) |
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thread.start() |
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buffer = "" |
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for new_text in streamer: |
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buffer += new_text |
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yield buffer |
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chatbot = gr.Chatbot(height=500) |
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with gr.Blocks() as demo: |
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gr.HTML(TITLE) |
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gr.HTML(DESCRIPTION) |
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gr.ChatInterface( |
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fn=stream_chat, |
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chatbot=chatbot, |
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fill_height=True, |
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theme="soft", |
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retry_btn=None, |
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undo_btn="Delete Previous", |
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clear_btn="Clear", |
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additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), |
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additional_inputs=[ |
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gr.Slider( |
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minimum=0, |
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maximum=1, |
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step=0.1, |
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value=0.8, |
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label="Temperature", |
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render=False, |
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), |
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gr.Slider( |
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minimum=128, |
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maximum=4096, |
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step=1, |
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value=1024, |
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label="Max new tokens", |
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render=False, |
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), |
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gr.Slider( |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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value=0.8, |
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label="top_p", |
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render=False, |
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), |
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gr.Slider( |
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minimum=1, |
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maximum=20, |
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step=1, |
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value=20, |
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label="top_k", |
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render=False, |
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), |
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gr.Slider( |
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minimum=0.0, |
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maximum=2.0, |
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step=0.1, |
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value=1.0, |
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label="Repetition penalty", |
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render=False, |
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), |
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], |
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examples=[ |
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["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."], |
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["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."], |
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["Tell me a random fun fact about the Roman Empire."], |
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["Show me a code snippet of a website's sticky header in CSS and JavaScript."], |
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], |
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cache_examples=False, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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