import torch from PIL import Image import gradio as gr import spaces from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer import os from threading import Thread HF_TOKEN = os.environ.get("HF_TOKEN", None) MODEL_ID = "CohereForAI/aya-23-8B" MODEL_ID2 = "CohereForAI/aya-23-35B" MODEL_NAME = MODEL_ID2.split("/")[-1] TITLE = "

Aya-23-Chatbox

" DESCRIPTION = f'

MODEL: {MODEL_NAME}

' CSS = """ .duplicate-button { margin: auto !important; color: white !important; background: black !important; border-radius: 100vh !important; } """ #QUANTIZE QUANTIZE_4BIT = True USE_GRAD_CHECKPOINTING = True TRAIN_BATCH_SIZE = 2 TRAIN_MAX_SEQ_LENGTH = 512 USE_FLASH_ATTENTION = False GRAD_ACC_STEPS = 16 quantization_config = None if QUANTIZE_4BIT: quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16, ) attn_implementation = None if USE_FLASH_ATTENTION: attn_implementation="flash_attention_2" model = AutoModelForCausalLM.from_pretrained( MODEL_ID2, quantization_config=quantization_config, attn_implementation=attn_implementation, torch_dtype=torch.bfloat16, device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained(MODEL_ID2) @spaces.GPU def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int): print(f'message is - {message}') print(f'history is - {history}') conversation = [] for prompt, answer in history: conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}]) conversation.append({"role": "user", "content": message}) print(f"Conversation is -\n{conversation}") input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) streamer = TextIteratorStreamer(tokenizer, **{"skip_special_tokens": True, "skip_prompt": True, 'clean_up_tokenization_spaces':False,}) generate_kwargs = dict( input_ids=input_ids, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature, ) thread = Thread(target=model.generate, kwargs=generate_kwargs) thread.start() buffer = "" for new_text in streamer: buffer += new_text yield buffer chatbot = gr.Chatbot(height=450) with gr.Blocks(css=CSS) as demo: gr.HTML(TITLE) gr.HTML(DESCRIPTION) gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") gr.ChatInterface( fn=stream_chat, chatbot=chatbot, fill_height=True, additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), additional_inputs=[ gr.Slider( minimum=0, maximum=1, step=0.1, value=0.8, label="Temperature", render=False, ), gr.Slider( minimum=128, maximum=4096, step=1, value=1024, label="Max new tokens", render=False, ), ], examples=[ ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."], ["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."], ["Tell me a random fun fact about the Roman Empire."], ["Show me a code snippet of a website's sticky header in CSS and JavaScript."], ], cache_examples=False, ) if __name__ == "__main__": demo.launch()