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-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
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- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    Texify latex image ocr
-    Copyright (C) 2023  Vikas Paruchuri
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    Texify  Copyright (C) 2023  Vikas Paruchuri
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,10 +0,0 @@
----
-license: mit
-title: Tonic's NV-Embed
-sdk: gradio
-emoji: n📽️n🛌🏻
-colorFrom: pink
-colorTo: green
-pinned: true
-app_file: app.py
----

app.py

@@ -1,193 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import Tensor
-from transformers import AutoTokenizer, AutoModel
-import threading
-import queue
-import gradio as gr
-import os
-import json
-import numpy as np
-
-
-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 👻  [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](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 = {
-        'ArguAna': 'Given a claim, find documents that refute the claim',
-        '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',
-        'SciFact': 'Given a scientific claim, retrieve documents that support or refute the claim',
-        'Touche2020': 'Given a question, retrieve detailed and persuasive arguments that answer the question',
-        'Natural Language Inference' : 'Retrieve semantically similar text',
-        'Natural Language Inference' : 'Given a premise, retrieve a hypothesis that is entailed by the premise 20k',
-        'PAQ, MSMARCO' : 'Given a web search query, retrieve relevant passages that answer the query',
-        'PAQ, MSMARCO' : 'Given a question, retrieve passages that answer the question',
-        'SQUAD' : 'Given a question, retrieve Wikipedia passages that answer the question' ,
-        'StackExchange' : 'Given a question paragraph at StackExchange, retrieve a question duplicated paragraph',
-        'Natural Question' : 'Given a question, retrieve Wikipedia passages that answer the question',
-        'BioASQ' : 'Given a question, retrieve detailed question descriptions that are duplicates to the given question',
-        'STS12, STS22, STSBenchmark' : 'Retrieve semantically similar text.',
-        'AmazonCounterfactualClassification' : 'Classify a given Amazon customer review text as either counterfactual or not-counterfactual' , 
-        'AmazonReviewsClassification' : 'Classify the given Amazon review into its appropriate rating category' , 
-        'Banking77Classification' : 'Given a online banking query, find the corresponding intents',
-        'EmotionClassification' : 'Classify the emotion expressed in the given Twitter message into one of the six emotions:anger, fear, joy, love, sadness, and surprise',
-        'ImdbClassification': 'Classify the sentiment expressed in the given movie review text from the IMDB dataset',
-        'MTOPIntentClassification' : 'Classify the intent of the given utterance in task-oriented conversation',
-        'ToxicConversationsClassification' : 'Classify the given comments as either toxic or not toxic',
-        'TweetSentimentExtractionClassification' : 'Classify the sentiment of a given tweet as either positive, negative, or neutral',
-        'ArxivClusteringP2P' : 'Identify the main and secondary category of Arxiv papers based on the titles and abstracts',
-        'ArxivClusteringS2S' : 'Identify the main and secondary category of Arxiv papers based on the titles',
-        'BiorxivClusteringP2P' : 'Identify the main category of Biorxiv papers based on the titles and abstracts' ,
-        'BiorxivClusteringS2S' : 'Identify the main category of Biorxiv papers based on the titles',
-        'MedrxivClusteringP2P' : 'Identify the main category of Medrxiv papers based on the titles and abstracts',
-        'MedrxivClusteringS2S' : 'Identify the main category of Medrxiv papers based on the titles',
-        'TwentyNewsgroupsClustering' : 'Identify the topic or theme of the given news articles'
-}
-
-os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:30'
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Define the model and tokenizer globally
-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)
-
-# Embedding requests and response queues
-embedding_request_queue = queue.Queue()
-embedding_response_queue = queue.Queue()
-
-def clear_cuda_cache():
-    torch.cuda.empty_cache()
-
-def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
-    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
-    if left_padding:
-        return last_hidden_states[:, -1]
-    else:
-        sequence_lengths = attention_mask.sum(dim=1) - 1
-        batch_size = last_hidden_states.shape[0]
-        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
-
-def format_response(embeddings):
-    return {
-        "data": [
-            {
-                "embedding": embeddings,
-                "index": 0,
-                "object": "embedding"
-            }
-        ],
-        "model": "e5-mistral",
-        "object": "list",
-        "usage": {
-            "prompt_tokens": 17,
-            "total_tokens": 17
-        }
-    }
-
-def embedding_worker():
-    while True:
-        # Wait for an item in the queue
-        item = embedding_request_queue.get()
-        if item is None:
-            break
-        selected_task, input_text = item
-        embeddings = compute_embeddings(selected_task, input_text)
-        formatted_response = format_response(embeddings)
-
-        embedding_response_queue.put(formatted_response)
-        embedding_request_queue.task_done()
-        clear_cuda_cache()
-
-def compute_embeddings(selected_task, input_text):
-    try:
-        task_description = tasks[selected_task]
-    except KeyError:
-        print(f"Selected task not found: {selected_task}")
-        return f"Error: Task '{selected_task}' not found. Please select a valid task."
-    
-    max_length = 2048
-    processed_texts = [f'Instruct: {task_description}\nQuery: {input_text}']
-    
-    batch_dict = tokenizer(processed_texts, max_length=max_length - 1, return_attention_mask=False, padding=False, truncation=True)
-    batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']]
-    batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt')
-    batch_dict = {k: v.to(device) for k, v in batch_dict.items()}
-    outputs = model(**batch_dict)
-    embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
-    embeddings = F.normalize(embeddings, p=2, dim=1)
-    embeddings_list = embeddings.detach().cpu().numpy().tolist()
-    clear_cuda_cache()
-    return embeddings_list
-
-def compute_similarity(selected_task, sentence1, sentence2, extra_sentence1, extra_sentence2):
-    try:
-        task_description = tasks[selected_task]
-    except KeyError:
-        print(f"Selected task not found: {selected_task}")
-        return f"Error: Task '{selected_task}' not found. Please select a valid task."
-    
-    # Compute embeddings for each sentence
-    embeddings1 = compute_embeddings(selected_task, sentence1)
-    embeddings2 = compute_embeddings(selected_task, sentence2)
-    embeddings3 = compute_embeddings(selected_task, extra_sentence1)
-    embeddings4 = compute_embeddings(selected_task, extra_sentence2)
-    
-    similarity1 = compute_cosine_similarity(embeddings1, embeddings2)
-    similarity2 = compute_cosine_similarity(embeddings1, embeddings3)
-    similarity3 = compute_cosine_similarity(embeddings1, embeddings4)
-
-    similarity_scores = {"Similarity 1-2": similarity1, "Similarity 1-3": similarity2, "Similarity 1-4": similarity3}
-    clear_cuda_cache()
-    return similarity_scores
-
-def compute_cosine_similarity(emb1, emb2):
-    tensor1 = torch.tensor(emb1).to(device).half()
-    tensor2 = torch.tensor(emb2).to(device).half()
-    similarity = F.cosine_similarity(tensor1, tensor2).item()
-    clear_cuda_cache()
-    return similarity
-
-def app_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown(title)
-        gr.Markdown(description)
-
-        with gr.Row():
-            task_dropdown = gr.Dropdown(list(tasks.keys()), label="Select a Task", value=list(tasks.keys())[0])
-
-        with gr.Tab("Sentence Similarity"):
-            sentence1_box = gr.Textbox(label="'Focus Sentence' - The 'Subject'")
-            sentence2_box = gr.Textbox(label="'Input Sentence' - 1")
-            extra_sentence1_box = gr.Textbox(label="'Input Sentence' - 2")
-            extra_sentence2_box = gr.Textbox(label="'Input Sentence' - 3")
-            similarity_button = gr.Button("Compute Similarity")
-            similarity_output = gr.Textbox(label="🐣e5-mistral🛌🏻 Similarity Scores")
-
-            similarity_button.click(
-                fn=compute_similarity,
-                inputs=[task_dropdown, sentence1_box, sentence2_box, extra_sentence1_box, extra_sentence2_box],
-                outputs=similarity_output
-            )
-
-    return demo
-
-embedding_worker_thread = threading.Thread(target=embedding_worker, daemon=True)
-embedding_worker_thread.start()
-
-app_interface().queue()
-app_interface().launch(share=True)

requirements.txt

@@ -1,4 +0,0 @@
-torch
-transformers
-# flash-attn
-einops