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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import spaces | |
| import torch | |
| import torch.nn.functional as F | |
| from torch.nn import DataParallel | |
| from torch import Tensor | |
| from transformers import AutoTokenizer, AutoModel | |
| import threading | |
| import queue | |
| import os | |
| import json | |
| import numpy as np | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| ## Global Variables | |
| title = """ | |
| # 👋🏻Welcome to 🙋🏻♂️Tonic's 📽️Nvidia 🛌🏻Embed V-1 !""" | |
| description = """ | |
| You can use this Space to test out the current model [nvidia/NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1). 🐣a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark (MTEB benchmark)(as of May 24, 2024), with 56 tasks, encompassing retrieval, reranking, classification, clustering, and semantic textual similarity tasks. | |
| You can also use 📽️Nvidia 🛌🏻Embed V-1 by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/NV-Embed?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: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to 🌟 [MultiTonic](https://github.com/MultiTonic) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗 | |
| """ | |
| tasks = { | |
| 'ClimateFEVER': 'Given a claim about climate change, retrieve documents that support or refute the claim', | |
| 'DBPedia': 'Given a query, retrieve relevant entity descriptions from DBPedia', | |
| 'FEVER': 'Given a claim, retrieve documents that support or refute the claim', | |
| 'FiQA2018': 'Given a financial question, retrieve user replies that best answer the question', | |
| 'HotpotQA': 'Given a multi-hop question, retrieve documents that can help answer the question', | |
| 'MSMARCO': 'Given a web search query, retrieve relevant passages that answer the query', | |
| 'NFCorpus': 'Given a question, retrieve relevant documents that best answer the question', | |
| 'NQ': 'Given a question, retrieve Wikipedia passages that answer the question', | |
| 'QuoraRetrieval': 'Given a question, retrieve questions that are semantically equivalent to the given question', | |
| 'SCIDOCS': 'Given a scientific paper title, retrieve paper abstracts that are cited by the given paper', | |
| } | |
| intention_prompt= """ | |
| "type": "object", | |
| "properties": { | |
| "ClimateFEVER": { | |
| "type": "boolean", | |
| "description" : "select this for climate science related text" | |
| }, | |
| "DBPedia": { | |
| "type": "boolean", | |
| "description" : "select this for encyclopedic related knowledge" | |
| }, | |
| "FEVER": { | |
| "type": "boolean", | |
| "description": "select this to verify a claim or embed a claim" | |
| }, | |
| "FiQA2018": { | |
| "type": "boolean", | |
| "description" : "select this for financial questions or topics" | |
| }, | |
| "HotpotQA": { | |
| "type": "boolean", | |
| "description" : "select this for a multi-hop question or for texts that provide multihop claims" | |
| }, | |
| "MSMARCO": { | |
| "type": "boolean", | |
| "description": "Given a web search query, retrieve relevant passages that answer the query" | |
| }, | |
| "NFCorpus": { | |
| "type": "boolean", | |
| "description" : "Given a question, retrieve relevant documents that best answer the question" | |
| }, | |
| "NQ": { | |
| "type": "boolean", | |
| "description" : "Given a question, retrieve Wikipedia passages that answer the question" | |
| }, | |
| "QuoraRetrieval": { | |
| "type": "boolean", | |
| "description": "Given a question, retrieve questions that are semantically equivalent to the given question" | |
| }, | |
| "SCIDOCS": { | |
| "type": "boolean", | |
| "description": "Given a scientific paper title, retrieve paper abstracts that are cited by the given paper" | |
| } | |
| }, | |
| "required": [ | |
| "ClimateFEVER", | |
| "DBPedia", | |
| "FEVER", | |
| "FiQA2018", | |
| "HotpotQA", | |
| "MSMARCO", | |
| "NFCorpus", | |
| "NQ", | |
| "QuoraRetrieval", | |
| "SCIDOCS", | |
| ] | |
| produce a complete json schema." | |
| you will recieve a text , classify the text according to the schema above. ONLY PROVIDE THE FINAL JSON , DO NOT PRODUCE ANY ADDITION INSTRUCTION :""" | |
| ## add chroma vector store | |
| ## use instruct embeddings | |
| # Load the tokenizer and model | |
| tokenizer = AutoTokenizer.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True) | |
| model = AutoModel.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True).to(device) | |
| ## Make intention Mapper | |
| ## Change to Yi API Client | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| messages = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |