import spaces import gradio as gr import torch import transformers from transformers import AutoModelForCausalLM, AutoTokenizer import os title = """# Welcome to 🌟Tonic's🐇🥷🏻Trinity You can build with this endpoint using🐇🥷🏻Trinity available here : [WhiteRabbitNeo/Trinity-13B](https://huggingface.co.//WhiteRabbitNeo/Trinity-13B). You can also use 🐇🥷🏻Trinity by cloning this space. Simply click here: Duplicate Space Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface:[MultiTransformer](https://huggingface.co./MultiTransformer) Math 🔍 [introspector](https://huggingface.co./introspector) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [SciTonic](https://github.com/Tonic-AI/scitonic)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗 """ default_system_prompt = """ Answer the Question by exploring multiple reasoning paths as follows: - First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree. - For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts. - Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option. Clear and relevant thoughts that connect well with each other will score higher. - Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order. - If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts. - Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal. - Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer. - Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process. In summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers. """ model_path = "/home/migel/models/WhiteRabbitNeo" hf_token = os.getenv("HF_TOKEN") if not hf_token: raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.") model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_8bit=True, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) @spaces.GPU def generate_text(custom_prompt, user_input, temperature, generate_len, top_p, top_k): system_prompt = custom_prompt if custom_prompt else default_system_prompt llm_prompt = f"{system_prompt} \nUSER: {user_input} \nASSISTANT: " tokens = tokenizer.encode(llm_prompt, return_tensors="pt") tokens = tokens.to("cuda") length = tokens.shape[1] with torch.no_grad(): output = model.generate( input_ids=tokens, max_length=length + generate_len, temperature=temperature, top_p=top_p, top_k=top_k, num_return_sequences=1, ) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) answer = generated_text[len(llm_prompt):].strip() return answer def gradio_app(): with gr.Blocks() as demo: gr.Markdown(title) with gr.Row(): custom_prompt = gr.Textbox(label="Custom System Prompt (optional)", placeholder="Leave blank to use the default prompt...") instruction = gr.Textbox(label="Your Instruction", placeholder="Type your question here...") with gr.Row(): temperature = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.5, label="Temperature") generate_len = gr.Slider(minimum=100, maximum=1024, step=10, value=100, label="Generate Length") top_p = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=1.0, label="Top P") top_k = gr.Slider(minimum=0, maximum=100, step=1, value=50, label="Top K") with gr.Row(): generate_btn = gr.Button("Generate") output = gr.Textbox(label="Generated Text", lines=10, placeholder="Generated answer will appear here...") generate_btn.click( fn=generate_text, inputs=[custom_prompt, instruction, temperature, generate_len, top_p, top_k], outputs=output ) demo.launch() if __name__ == "__main__": gradio_app()