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Hugging Face Publishes Guide on Efficient LLM Training across GPUs
Hugging Face has published the Ultra-Scale Playbook: Training LLMs on GPU Clusters, an open-source guide that provides a detailed exploration of the methodologies and technologies involved in training LLMs across GPU clusters.
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Hugging Face Expands Serverless Inference Options with New Provider Integrations
Hugging Face has launched the integration of four serverless inference providers Fal, Replicate, SambaNova, and Together AI, directly into its model pages. These providers are also integrated into Hugging Face's client SDKs for JavaScript and Python, allowing users to run inference on various models with minimal setup.
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DeepSeek Release Another Open-Source AI Model, Janus Pro
DeepSeek has released Janus-Pro, an updated version of its multimodal model, Janus. The new model improves training strategies, data scaling, and model size, enhancing multimodal understanding and text-to-image generation.
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Synthetic Data Generator Simplifies Dataset Creation with Large Language Models
Hugging Face has introduced the Synthetic Data Generator, a new tool leveraging Large Language Models (LLMs), that offers a streamlined, no-code approach to creating custom datasets. The tool facilitates the creation of text classification and chat datasets through a clear and accessible process, making it usable for both non-technical users and experienced AI practitioners.
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Microsoft Phi-4 is a Small Language Model Specialized for Complex Math Reasoning
Phi-4 is 14B parameter model from Microsoft Research that aims to improve the state of the art for math reasoning. Previously available on Azure AI Foundry, Phi-4 has recently become available on Hugging Face under the MIT license.
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Hugging Face Smolagents is a Simple Library to Build LLM-Powered Agents
Smolagents is a library created at Hugging Face to build agents based on large language models (LLMs). Hugging Faces says its new library aims to be simple and LLM-agnostic. It supports secure "agents that write their actions in code" and is integrated with Hugging Face Hub.
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NVIDIA Unveils Hymba 1.5B: a Hybrid Approach to Efficient NLP Models
NVIDIA researchers have unveiled Hymba 1.5B, an open-source language model that combines transformer and state-space model (SSM) architectures to achieve unprecedented efficiency and performance. Designed with NVIDIA’s optimized training pipeline, Hymba addresses the computational and memory limitations of traditional transformers while enhancing the recall capabilities of SSMs.
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LLaMA-Mesh: NVIDIA’s Breakthrough in Unifying 3D Mesh Generation and Language Models
NVIDIA researchers have introduced LLaMA-Mesh, a groundbreaking approach that extends large language models (LLMs) to generate and interpret 3D mesh data in a unified, text-based framework. LLaMA-Mesh tokenizes 3D meshes as plain text, enabling the seamless integration of spatial and textual information.
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Hugging Face and Entalpic Unveil LeMaterial: Transforming Materials Science through AI
Entalpic, in collaboration with Hugging Face, has launched LeMaterial, an open-source initiative to tackle key challenges in materials science. By unifying data from major resources into LeMat-Bulk, a harmonized dataset with 6.7 million entries, LeMaterial aims to streamline materials discovery and accelerate innovation in areas such as LEDs, batteries, and photovoltaic cells.
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Meta Releases Llama 3.3: a Multilingual Model with Enhanced Performance and Efficiency
Meta has released Llama 3.3, a multilingual large language model aimed at supporting a range of AI applications in research and industry. Featuring a 128k-token context window and architectural improvements for efficiency, the model demonstrates strong performance in benchmarks for reasoning, coding, and multilingual tasks. It is available under a community license on Hugging Face.
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Mistral AI Releases Pixtral Large: a Multimodal Model for Advanced Image and Text Analysis
Mistral AI released Pixtral Large, a 124-billion-parameter multimodal model designed for advanced image and text processing with a 1-billion-parameter vision encoder. Built on Mistral Large 2, it achieves leading performance on benchmarks like MathVista and DocVQA, excelling in tasks that require reasoning across text and visual data.
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Hugging Face Launches SmolTools: Practical AI Apps Powered by SmolLM2 Model
Hugging Face has introduced SmolTools, a set of applications built on the recently launched SmolLM2 model, a compact 1.7-billion parameter language model. SmolTools includes specialized tools for summarization, rewriting, and task automation, bringing efficient AI functionality to a broader range of users.
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NVIDIA Unveils NVLM 1.0: Open-Source Multimodal LLM with Improved Text and Vision Capabilities
NVIDIA unveiled NVLM 1.0, an open-source multimodal large language model (LLM) that performs strongly on both vision-language and text-only tasks. NVLM 1.0 shows improvements in text-based tasks after multimodal training, standing out among current models. The model weights are now available on Hugging Face, with the training code set to be released shortly.
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Hugging Face Upgrades Open LLM Leaderboard v2 for Enhanced AI Model Comparison
Hugging Face has recently released Open LLM Leaderboard v2, an upgraded version of their benchmarking platform for large language models. Hugging Face created the Open LLM Leaderboard to provide a standardized evaluation setup for reference models, ensuring reproducible and comparable results.
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NVIDIA NIM Now Available on Hugging Face with Inference-as-a-Service
Hugging Face has announced the launch of an inference-as-a-service capability powered by NVIDIA NIM. This new service will provide developers easy access to NVIDIA-accelerated inference for popular AI models.