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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, BitsAndBytesConfig, 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 = "CohereForAI/aya-23-8B" |
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MODEL_ID2 = "CohereForAI/aya-23-35B" |
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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>Aya-23-Chatbox</center></h1>" |
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DESCRIPTION = f'<h3><center>MODEL: <a href="https://hf.co/{MODELS}">{MODEL_NAME}</a></center></h3>' |
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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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""" |
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QUANTIZE_4BIT = True |
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USE_GRAD_CHECKPOINTING = True |
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TRAIN_BATCH_SIZE = 2 |
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TRAIN_MAX_SEQ_LENGTH = 512 |
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USE_FLASH_ATTENTION = False |
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GRAD_ACC_STEPS = 16 |
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quantization_config = None |
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if QUANTIZE_4BIT: |
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quantization_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_compute_dtype=torch.bfloat16, |
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) |
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attn_implementation = None |
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if USE_FLASH_ATTENTION: |
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attn_implementation="flash_attention_2" |
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model = AutoModelForCausalLM.from_pretrained( |
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MODELS, |
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quantization_config=quantization_config, |
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attn_implementation=attn_implementation, |
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torch_dtype=torch.bfloat16, |
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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): |
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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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print(f"Conversation is -\n{conversation}") |
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input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) |
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streamer = TextIteratorStreamer(tokenizer, **{"skip_special_tokens": True, "skip_prompt": True, 'clean_up_tokenization_spaces':False,}) |
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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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temperature=temperature, |
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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=450) |
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with gr.Blocks(css=CSS) as demo: |
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gr.HTML(TITLE) |
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gr.HTML(DESCRIPTION) |
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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=0.8, |
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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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], |
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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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