NEW: tiny-agents now supports AGENTS.md standard. 🥳 Inspired by Tiny Agents in JS, we ported the idea to Python 🐍 and extended the huggingface_hub client SDK to act as a MCP Client so it can pull tools from MCP servers and pass them to the LLM during inference. MCP (Model Context Protocol) is an open protocol that standardizes how Large Language Models (LLMs) interact with external tools and APIs. Essentially, it removed the need to write custom integrations for each […]

This week at Dell Tech World, we announced the new version of Dell Enterprise Hub, with a complete suite of models and applications to easily build AI running on premises with Dell AI servers and AI PCs. Models Ready for Action If you go to the Dell Enterprise Hub today, you can find some of the most popular models, like Meta Llama 4 Maverick,    

Thank you for your great work. Anyway, I tested the liger loss with deepspeed zero3 using Qwen/Qwen2.5-0.5B-Instruct in a bf16.I met an shape mismatch as stated below: [rank0]: Traceback (most recent call last): [rank0]: File “/workspace/temp.py”, line 22, in [rank0]: trainer.train() [rank0]: File “/usr/local/lib/python3.11/dist-packages/transformers/trainer.py”, line 2238, in train [rank0]: return inner_training_loop( [rank0]: ^^^^^^^^^^^^^^^^^^^^ [rank0]: File “/usr/local/lib/python3.11/dist-packages/transformers/trainer.py”, line 2553, in _inner_training_loop [rank0]: tr_loss_step = self.training_step(model, inputs, num_items_in_batch) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File “/usr/local/lib/python3.11/dist-packages/transformers/trainer.py”, line 3730, in training_step [rank0]: loss = self.compute_loss(model, inputs, […]

Today we’re sharing research that bridges two powerful paradigms in AI agent design: the expressiveness of code-based actions and the reliability of structured generation. Our findings show that forcing CodeAgents to generate both thoughts and code in    

TRL supports training LLMs using GRPO, an online learning algorithm recently introduced in the DeepSeekMath paper. In GRPO, the model learns from its own outputs: it generates responses during training, receives feedback, and uses that feedback to improve itself over time. This makes generation a critical step in the training loop — and also a major bottleneck. To speed up generation, TRL integrates with vLLM. This combination lets you train powerful models more efficiently in GRPO setup. However, there’s a […]

Today, we introduce SmolVLA, a compact (450M), open-source Vision-Language-Action model for robotics that runs on consumer hardware. Pretrained only on compatibly licensed, open-source community-shared datasets under the lerobot tag. SmolVLA-450M outperforms much larger VLAs and strong baselines such as ACT on simulation (LIBERO, Meta-World) and real-world tasks (SO100, SO101). Supports asynchronous inference for 30% faster response and 2× task throughput. Useful links: 📚 Table of Contents

Today, at H Company, we are releasing Holo1, a family of Action Vision Language Models (VLMs) and WebClick, a new multimodal localization benchmark on the Hugging Face Hub. Surfer-H, a web-native    

By Michael Gamble, Partner & Ecosystem Lead, Arm As a software engineer and music producer, I’m always exploring how technology can expand creative expression. That curiosity recently led me to build a personal sound generation app    

We have implemented KV Caching from scratch in our nanoVLM repository (a small codebase to train your own Vision Language Model with pure PyTorch). This gave us a 38% speedup in generation. In this blog post we cover KV Caching and all our experiences while implementing it. The lessons learnt are general and can be applied to all autoregressive language model generations. Implementing from scratch on a small codebase is a great learning experience, come along for the ride!

Today at GTC Paris, we are excited to announce Training Cluster as a Service in collaboration with NVIDIA, to make large GPU clusters more easily accessible for research organizations all over the world, so they can train the foundational models of tomorrow in every domain. Making GPU Clusters Accessible Many Gigawatt-size GPU supercluster projects are being built to train the next gen of AI models. This can make it seem that the compute gap between the “GPU