huggingface

A modern platform for competitive programming.

KV cache middleware for 1M context on 12GB VRAM. Uses K vectors as a retrieval index to fetch V on-demand from system RAM. No model modification, no retraining. Drop-in HuggingFace cache replacement.

An Amazon SageMaker Container for Hugging Face Inference on Graviton and Intel CPUs

Development and deployment on AWS of a question-answer LLM model using Llama2 with 7B parameters and RAG with LangChain

Billus 大模型技能库：面向 LLM、VL、多模态与图像生成模型的训练、调参、剪枝、量化与工程化技能库

Multimodal Document Processing RAG with LangChain

BobVLM – A 1.5B multimodal model built from scratch and pre-trained on a single P100 GPU capable of image descriptions and moderate question answering. 🤗🎉

Simple repo to finetune an LLM hosted on Hugging Face by creating a LORA

CLI for exporting Claude Code/Codex sessions to Hugging Face with privacy redaction, MCP memory, and share-ready dataset workflows.

notebooks to finetune `bert-small-amharic`, `bert-mini-amharic`, and `xlm-roberta-base` models using an Amharic text classification dataset and the transformers library

Detecting fake news articles by analyzing patterns in writing.

Manage HuggingFace models with Python desktop app

DistilBERT model with Sequence Classification on 9 newsgroup datasets for the Nepali language with 95.475% accuracy.

An interactive CLI tool that leverages Ollama, ChatGPT, Gemini, Claude, Mistral and other AI.

About Advanced AI-Generated Image Detection System using EfficientNetB7 with Custom Attention Mechanism. Developed for AICS 2025 (33rd Irish Conference on Artificial Intelligence and Cognitive Science). Detects deepfakes from GANs, Diffusion Models, and other generative AI.

The most capable Calibre MCP server — full read/write tools plus multilingual semantic search over your whole ebook library or a single book. For Claude & any MCP client.

Experimental tl;dr summaries for datasets on the Hugging Face Hub!

BERT for extractive summarization using HuggingFace

Different post-training techniques for LLMs, including: SFT, DPO and Online RL

Companion code for the Manning book 'LLM Customization and Fine-Tuning.' Adapt open-weights LLMs end to end: prompting and RAG, LoRA/QLoRA, full SFT, distillation, and DPO/RLHF alignment.