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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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import gradio as gr |
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from threading import Thread |
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MODEL = "tiiuae/Falcon3-7B-Instruct-1.58bit" |
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TITLE = "<h1><center>Falcon3-1.58bit-instruct playground</center></h1>" |
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SUB_TITLE = """<center>This interface has been created for quick validation purposes, do not use it for production.</center>""" |
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CSS = """ |
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.duplicate-button { |
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margin: auto !important; |
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color: white !important; |
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background: black !important; |
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border-radius: 100vh !important; |
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} |
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h3 { |
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text-align: center; |
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} |
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""" |
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END_MESSAGE = """ |
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\n |
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**The conversation has reached to its end, please press "Clear" to restart a new conversation** |
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""" |
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device = "cuda" |
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tokenizer = AutoTokenizer.from_pretrained(MODEL) |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL, |
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torch_dtype=torch.bfloat16, |
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).to(device) |
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model = torch.compile(model) |
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def stream_chat( |
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message: str, |
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history: list, |
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temperature: float = 0.3, |
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max_new_tokens: int = 128, |
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top_p: float = 1.0, |
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top_k: int = 20, |
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penalty: float = 1.2, |
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): |
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print(f'message: {message}') |
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print(f'history: {history}') |
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conversation = [] |
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for prompt, answer in history: |
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conversation.extend([ |
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{"role": "user", "content": prompt}, |
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{"role": "assistant", "content": answer}, |
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]) |
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conversation.append({"role": "user", "content": message}) |
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input_text = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt = True) |
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) |
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streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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input_ids=inputs, |
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max_new_tokens = max_new_tokens, |
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do_sample = False if temperature == 0 else 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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streamer=streamer, |
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pad_token_id = 10, |
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) |
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with torch.no_grad(): |
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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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print(f'response: {buffer}') |
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chatbot = gr.Chatbot(height=600) |
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with gr.Blocks(css=CSS, theme="soft") as demo: |
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gr.HTML(TITLE) |
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gr.HTML(SUB_TITLE) |
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gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") |
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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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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=0.3, |
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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=128, |
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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=1.0, |
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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=1.2, |
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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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["Hello there, can you suggest few places to visit in UAE?"], |
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["What UAE is known for?"], |
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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() |
