Containerized self-hosted REST API for vision classification, utilizing Hugging Face transformers.
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Updated
Nov 9, 2024 - Python
Containerized self-hosted REST API for vision classification, utilizing Hugging Face transformers.
fine-tuning project with the LLaMA model: Llama-2-7b-chat-finetune This repository provides an implementation for fine-tuning the LLaMA-2-7b model on conversational datasets utilizing the
Hybrid Retrieval System combining keyword matching (BM25) with semantic similarity (Vectorstore) for improved retrieval.
ClassyText is a demo for zero-shot text classification using DeBERTa-v3-large from Hugging Face.
Containerized self-hosted REST API for interacting with Hugging Face NLP models.
The Gemini-Pro Sentiment prediction Chatbot is a smart conversational app powered by the Gemini-Pro LLM. It uses state-of-the-art NLP to grasp user queries, provide relevant responses, and predict sentiment using the Hugging Face model cardiffnlp/twitter-roberta-base-sentiment-latest.
Getting started with Hugging Face
Render a sketch using a Huggingface pipeline
A Turkish question answering system made by fine-tuning BERTurk and XLM-Roberta models.
A Turkish question answering system made by fine-tuning BERTurk and XLM-Roberta models.
HFDownloader - Hugging Face Model Downloader
HFLoader - HuggingFace Model Loader
Running Stable diffusion from a mobile phone using Termux
A RESTful API that embeds classification with confidence of textual suicidal thoughts, conducted on a previously compiled transformer model, utilizing Python
This is the avishkaarak-ekta-hindi model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
PoC for HuggingFace Multi-worker Server
Project predicting geolocation from tweets as part of Yachay.ai externship
This is a project done for an assessment. I found it to be interesting and decided to share this. The idea is to create a scraper to scrap the Wikipedia page and generate question and answers
Template to serve, optimize and scale 🤗 pipeline in production with Torchserve & Docker
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