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| 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: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/trinity?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> | |
| Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [](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) | |
| 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() | |