<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Huggingface on Noureddine RAMDI</title><link>https://ramdi.fr/tags/huggingface/</link><description>Recent content in Huggingface on Noureddine RAMDI</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 23 May 2026 20:41:27 +0000</lastBuildDate><atom:link href="https://ramdi.fr/tags/huggingface/index.xml" rel="self" type="application/rss+xml"/><item><title>hf-agents: a shell CLI extension for hardware-aware local coding agents with llama.cpp</title><link>https://ramdi.fr/github-stars/hf-agents-a-shell-cli-extension-for-hardware-aware-local-coding-agents-with-llama-cpp/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/hf-agents-a-shell-cli-extension-for-hardware-aware-local-coding-agents-with-llama-cpp/</guid><description>hf-agents automates hardware profiling, model selection, and local coding agent deployment using llama.cpp and Pi, all in a shell CLI extension. Efficient and minimal dependencies.</description></item><item><title>tribev2: pretrained models for predicting brain responses to videos</title><link>https://ramdi.fr/github-stars/tribev2-pretrained-models-for-predicting-brain-responses-to-videos/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/tribev2-pretrained-models-for-predicting-brain-responses-to-videos/</guid><description>tribev2 offers pretrained models to predict brain responses to videos using cortical mesh modeling. Supports video, text, and audio inputs with easy inference setup.</description></item><item><title>MegaTrain: RAM-centric training architecture for 100B+ parameter LLMs on a single GPU</title><link>https://ramdi.fr/github-stars/megatrain-ram-centric-training-architecture-for-100b-parameter-llms-on-a-single-gpu/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/megatrain-ram-centric-training-architecture-for-100b-parameter-llms-on-a-single-gpu/</guid><description>MegaTrain enables training 100B+ parameter LLMs on a single GPU by offloading all parameters to CPU RAM and streaming layers to GPU. Supports HuggingFace models and multi-GPU data parallelism without NCCL.</description></item><item><title>Hugging Face Transformers: a unified API for state-of-the-art AI models across modalities</title><link>https://ramdi.fr/github-stars/hugging-face-transformers-a-unified-api-for-state-of-the-art-ai-models-across-modalities/</link><pubDate>Sun, 26 Apr 2026 09:31:26 +0000</pubDate><guid>https://ramdi.fr/github-stars/hugging-face-transformers-a-unified-api-for-state-of-the-art-ai-models-across-modalities/</guid><description>Hugging Face Transformers offers a unified Python API to access over 1 million pretrained AI models for text, vision, and audio, simplifying complex pipelines with its Pipeline API.</description></item></channel></rss>