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import subprocess |
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subprocess.run( |
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'pip install flash-attn --no-build-isolation', |
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env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, |
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shell=True |
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) |
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import os |
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import re |
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import time |
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import torch |
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import spaces |
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import gradio as gr |
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from threading import Thread |
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from transformers import ( |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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BitsAndBytesConfig, |
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TextIteratorStreamer |
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) |
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MODEL_ID = "CohereForAI/aya-expanse-32b" |
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DEFAULT_SYSTEM_PROMPT = """You are a highly intelligent Bilingual assistant who is fluent in Arabic and English.""" |
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TITLE = "<h1><center>Mawared T Assistant</center></h1>" |
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PLACEHOLDER = "Ask me anything! I'll think through it step by step." |
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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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.message-wrap { |
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overflow-x: auto; |
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} |
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.message-wrap p { |
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margin-bottom: 1em; |
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} |
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.message-wrap pre { |
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background-color: #f6f8fa; |
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border-radius: 3px; |
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padding: 16px; |
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overflow-x: auto; |
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} |
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.message-wrap code { |
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background-color: rgba(175,184,193,0.2); |
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border-radius: 3px; |
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padding: 0.2em 0.4em; |
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font-family: monospace; |
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} |
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.custom-tag { |
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color: #0066cc; |
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font-weight: bold; |
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} |
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.chat-area { |
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height: 500px !important; |
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overflow-y: auto !important; |
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} |
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""" |
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def initialize_model(): |
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"""Initialize the model with appropriate configurations""" |
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quantization_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_compute_dtype=torch.bfloat16, |
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bnb_4bit_use_double_quant=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
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if tokenizer.pad_token_id is None: |
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tokenizer.pad_token_id = tokenizer.eos_token_id |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_ID, |
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torch_dtype=torch.float16, |
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device_map="cuda", |
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attn_implementation="flash_attention_2", |
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quantization_config=quantization_config |
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) |
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return model, tokenizer |
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def format_text(text): |
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"""Format text with proper spacing and tag highlighting (but keep tags visible)""" |
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tag_patterns = [ |
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(r'<Thinking>', '\n<Thinking>\n'), |
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(r'</Thinking>', '\n</Thinking>\n'), |
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(r'<Critique>', '\n<Critique>\n'), |
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(r'</Critique>', '\n</Critique>\n'), |
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(r'<Revising>', '\n<Revising>\n'), |
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(r'</Revising>', '\n</Revising>\n'), |
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(r'<Final>', '\n<Final>\n'), |
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(r'</Final>', '\n</Final>\n') |
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] |
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formatted = text |
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for pattern, replacement in tag_patterns: |
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formatted = re.sub(pattern, replacement, formatted) |
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formatted = '\n'.join(line for line in formatted.split('\n') if line.strip()) |
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return formatted |
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def format_chat_history(history): |
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"""Format chat history for display, keeping tags visible""" |
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formatted = [] |
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for user_msg, assistant_msg in history: |
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formatted.append(f"User: {user_msg}") |
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if assistant_msg: |
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formatted.append(f"Assistant: {assistant_msg}") |
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return "\n\n".join(formatted) |
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def create_examples(): |
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"""Create example queries for the UI""" |
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return [ |
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"Explain the concept of artificial intelligence.", |
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"How does photosynthesis work?", |
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"What are the main causes of climate change?", |
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"Describe the process of protein synthesis.", |
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"What are the key features of a democratic government?", |
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"Explain the theory of relativity.", |
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"How do vaccines work to prevent diseases?", |
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"What are the major events of World War II?", |
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"Describe the structure of a human cell.", |
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"What is the role of DNA in genetics?" |
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] |
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@spaces.GPU(duration=660) |
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def chat_response( |
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message: str, |
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history: list, |
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chat_display: str, |
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system_prompt: str, |
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temperature: float = 1.0, |
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max_new_tokens: int = 4000, |
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top_p: float = 0.8, |
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top_k: int = 40, |
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penalty: float = 1.2, |
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): |
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"""Generate chat responses, keeping tags visible in the output""" |
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conversation = [ |
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{"role": "system", "content": system_prompt} |
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] |
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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_ids = tokenizer.apply_chat_template( |
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conversation, |
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add_generation_prompt=True, |
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return_tensors="pt" |
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).to(model.device) |
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streamer = TextIteratorStreamer( |
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tokenizer, |
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timeout=60.0, |
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skip_prompt=True, |
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skip_special_tokens=True |
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) |
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generate_kwargs = dict( |
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input_ids=input_ids, |
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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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repetition_penalty=penalty, |
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streamer=streamer, |
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) |
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buffer = "" |
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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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history = history + [[message, ""]] |
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for new_text in streamer: |
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buffer += new_text |
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formatted_buffer = format_text(buffer) |
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history[-1][1] = formatted_buffer |
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chat_display = format_chat_history(history) |
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yield history, chat_display |
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def process_example(example: str) -> tuple: |
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"""Process example query and return empty history and updated display""" |
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return [], f"User: {example}\n\n" |
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def main(): |
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"""Main function to set up and launch the Gradio interface""" |
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global model, tokenizer |
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model, tokenizer = initialize_model() |
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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.DuplicateButton( |
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value="Duplicate Space for private use", |
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elem_classes="duplicate-button" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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chat_history = gr.State([]) |
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chat_display = gr.TextArea( |
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value="", |
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label="Chat History", |
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interactive=False, |
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elem_classes=["chat-area"], |
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) |
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message = gr.TextArea( |
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placeholder=PLACEHOLDER, |
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label="Your message", |
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lines=3 |
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) |
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with gr.Row(): |
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submit = gr.Button("Send") |
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clear = gr.Button("Clear") |
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with gr.Accordion("⚙️ Advanced Settings", open=False): |
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system_prompt = gr.TextArea( |
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value=DEFAULT_SYSTEM_PROMPT, |
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label="System Prompt", |
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lines=5, |
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) |
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temperature = 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=0.2, |
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label="Temperature", |
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) |
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max_tokens = gr.Slider( |
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minimum=128, |
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maximum=32000, |
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step=128, |
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value=4000, |
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label="Max Tokens", |
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) |
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top_p = gr.Slider( |
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minimum=0.1, |
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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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) |
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top_k = gr.Slider( |
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minimum=1, |
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maximum=100, |
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step=1, |
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value=40, |
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label="Top-k", |
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) |
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penalty = gr.Slider( |
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minimum=1.0, |
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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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) |
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examples = gr.Examples( |
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examples=create_examples(), |
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inputs=[message], |
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outputs=[chat_history, chat_display], |
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fn=process_example, |
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cache_examples=False, |
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) |
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submit_click = submit.click( |
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chat_response, |
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inputs=[ |
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message, |
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chat_history, |
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chat_display, |
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system_prompt, |
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temperature, |
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max_tokens, |
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top_p, |
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top_k, |
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penalty, |
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], |
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outputs=[chat_history, chat_display], |
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show_progress=True, |
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) |
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message.submit( |
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chat_response, |
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inputs=[ |
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message, |
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chat_history, |
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chat_display, |
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system_prompt, |
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temperature, |
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max_tokens, |
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top_p, |
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top_k, |
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penalty, |
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], |
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outputs=[chat_history, chat_display], |
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show_progress=True, |
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) |
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clear.click( |
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lambda: ([], ""), |
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outputs=[chat_history, chat_display], |
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show_progress=True, |
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) |
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submit_click.then(lambda: "", outputs=message) |
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message.submit(lambda: "", outputs=message) |
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return demo |
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if __name__ == "__main__": |
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demo = main() |
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demo.launch() |
