v0.7.0
LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
llama.cpp11 min read
Hugging Face Blog
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Agents on live hardware: reason where you must, engineer everywhere else
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llama.cpp1 min read
Run fast, private agents on-device with LFM2.5-24B-A2B
LFM2.5-24B-A2B is a 24B-parameter reasoning model with just 2B active parameters, built for fast, private agents on-device. It thinks before it answers, calls tools, and works through multi-step tasks inside popular agent harnesses.
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Impactful scheduling for GPU clusters
Building a cluster scheduler to prioritize high-impact research while maintaining full occupancy
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The model that didn't exist, so you made it yourself
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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Multimodal open d1 decision models for the edge
Today, we release two open decision models in our d1 decision model family: d1-3B and d1-omni-600M (experimental).
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Introducing Falcon ASR
A Blog post by Technology Innovation Institute on Hugging Face
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One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO
The International Olympiad in Informatics (IOI) and the International Mathematical Olympiad (IMO) test different skills. IOI requires algorithms and code that pass hidden tests under strict time and submission limits. IMO demands rigorous natural-language proofs.
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Falcon-Emirati: When an LLM Learns the Dialect, the Culture, and the Nuance
A Blog post by Technology Innovation Institute on Hugging Face
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The Agent Said It Was Done. The Database Disagreed.
Microsoft ThinkingBox grades AI agents on the records they leave behind, not the sentences they generate, and then asks whether they can do it twenty times in a row. It is now available through Hugging Face.
Hugging Face Blog15 min read
Open-sourcing AstaBrief, the fast report-generation model in Asta
Language models can already help researchers search the literature, synthesize evidence, and work through complex questions. But scientific work places particular demands on these models—answers need to stay grounded in evidence, the models need to preserve what the evidence actually supports…
Hugging Face Blog12 min read
AutoSynthData: Generating Training Data for Enterprise Agents
Enterprises need agents that work well in their own environments. The work they ask these agents to do is shaped by the systems they use, the rules they follow, and the state of their data.
Hugging Face Blog10 min read
Introducing Olmo-core 3: Open, scalable training infrastructure for large MoEs
Today we’re releasing Olmo-core 3, a significant upgrade to our framework for developing large language models featuring a redesigned open mixture-of-experts (MoE) training system.
Hugging Face Blog6 min read
Open TTS Leaderboard: Scalable Evaluation for Multilingual Text-to-Speech and Voice Cloning
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction
NVIDIA Kumo Tabular, part of the NVIDIA Kumo Structured model collection, is an open foundation model for tabular data now available on Hugging Face. Given a table of labeled rows, it predicts the labels of new rows in a single forward pass, with no training, no tuning, and no feature engineering,…
Hugging Face Blog7 min read
Getting the Source Right, Not Just the Fact: Source-Aware Verification for MCP Agents
Our latest paper, ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents (read it on Hugging Face, or on arXiv in the meantime), targets that gap.
Hugging Face Blog8 min read
Holo4: powering generalist computer-use agents
Holo4 is our new series of agentic models. It comes in two sizes: 27B dense and 35B-A3B Mixture of Experts. Both are available on the H Models API. We are also releasing an updated version of Holotron 3: Holotron4 Nano.
Hugging Face Blog7 min read
Accelerating vision-language models with LFM2.5-VL-DSpark
Faster inference: decode speedups up to 3.13x on device and 2.66x on an H100, with end-to-end gains up to 2.62x and 2.27x.
Hugging Face Blog6 min read
Transformers now runs llama.cpp quants
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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How UK AISI and EvalEval Are Making Benchmark Results Reproducible
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Hugging Face Blog4 min read
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