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Hugging Face Datasets
Dataset loading and processing; tasks have crisp boundaries, making it one of the easiest HF repos to enter.
Hugging FaceAbout 2 hrpython
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Hugging Face
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Open sourceAIIntermediate
Gradio
The ML demo UI library, with beginner issues on both its Python and frontend sides.
Hugging FaceAbout 2 hrpythontypescript
Open sourceAIIntermediate
Transformers
Pretrained-model toolkit spanning text, vision, audio, and multimodal tasks.
Hugging FaceAbout 2 hrpythontransformers
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Hugging Face Blog
Official Hugging Face updates on open models, tools, and community technology.
Hugging Face
Official source · Hugging Face BlogVerified Aug 24, 2026
AI updateAI
Your Agent Aced the Task. Will It Do It Again?
That is embarrassing onstage. In production, it is a reliability problem: a workflow that succeeded once may fail the next time a user makes the same request. For mission-critical work, such as reconciling a financial transaction or checking a contract for an obligation, that can be a showstopper.
Hugging FacePublished Sep 16, 2026
Cross-checked · Hugging Face BlogVerified Oct 4, 2026
AI updateAI
NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction
The Shift to Tabular Foundation Models Tabular data is the backbone of enterprise machine learning. Customer records, transactions, sensor logs, claims, and orders all live in tables, and predicting churn, default, demand, or price from them is among the most common machine learning tasks in industry. For two decades,…
Hugging FacePublished Sep 29, 2026
Cross-checked · Hugging Face BlogVerified Oct 8, 2026
AI updateAI
Welcome RL Environments to the hub
An environment gives an agent a task, responds to its actions with observations, and scores the outcome. The resulting rewards can measure an agent's performance during evaluation or provide a learning signal during training. For an introduction to this interaction loop, see our blogpost on environments . Within the e…
Hugging FacePublished Sep 28, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
AI updateAI
tokenizers v1: encode, decode and scaling, measured
As models become faster and workloads scale, that balance begins to shift. Training on massive datasets, serving many concurrent requests, or repeatedly processing long inputs can put enough pressure on the tokenizer that it starves the model of data.
Hugging FacePublished Sep 21, 2026
Cross-checked · Hugging Face BlogVerified Oct 6, 2026
AI updateAI
Transformers now runs llama.cpp quants
Running AI models on your laptop has become much easier, and llama.cpp has been a big part of that. Its inference engine powers local AI tools such as Ollama, LM Studio, and Jan. Alongside projects like MLX , it has helped make local inference a practical option for everyday use.
Hugging FacePublished Sep 22, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
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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. The failure mode we care about is one we call cross-source conflation: a claim that is true somewhere in the evidence, but attributed to the wrong…
Hugging FacePublished Sep 29, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
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Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL
LoRA support recently landed in TRL's AsyncGRPOTrainer with PR #7017 , and ships with TRL v1.14. The asynchronous trainer can now train an adapter instead of the full model, and it syncs only the LoRA adapter to vLLM. This post covers a real-world project built on top of it, where training and inference no longer shar…
Hugging FacePublished Sep 10, 2026
Cross-checked · Hugging Face BlogVerified Oct 2, 2026
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Accelerating vision-language models with LFM2.5-VL-DSpark
How does speculative decoding work for VLMs The vision drafter uses the same architecture as our text LFM2.5-DSpark drafters: it captures the target model's hidden states at a fixed set of tapped layers and conditions on them to draft a block of k candidate tokens. Image patches and text tokens are projected into a sh…
Hugging FacePublished Sep 24, 2026
Cross-checked · Hugging Face BlogVerified Oct 8, 2026
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Open TTS Leaderboard: Scalable Evaluation for Multilingual Text-to-Speech and Voice Cloning
Evaluation, however, hasn't kept pace: it remains fragmented and unstandardized. The gold standard is human preference scores such as MOS or MUSHRA (more on metrics ). To this end, several arena-based leaderboards have established themselves as useful reference points for the community:
Hugging FacePublished Sep 30, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
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Introducing Falcon ASR
Arabic WER: 20.92% · Parameters: 1.6B · Emirati WER (TII evaluation): 22.73%
Hugging FacePublished Oct 7, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
AI updateAI
The Agent Said It Was Done. The Database Disagreed.
Figure 1: ThinkingBox runs an agent against isolated MCP tool sessions, then grades the terminal backend state and side effects it leaves behind. From our ThinkingBox paper .
Hugging FacePublished Oct 4, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
AI updateAI
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. A model may be broadly capable and still struggle with a particular environment: a workflow it handles poorly, a combination of t…
Hugging FacePublished Oct 2, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
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Holo4: powering generalist computer-use agents
The official source provides no summary. Open the item to read the original.
Hugging FacePublished Sep 28, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
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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 FacePublished Oct 1, 2026
Cross-checked · Hugging Face BlogVerified Oct 3, 2026
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Falcon-Emirati: When an LLM Learns the Dialect, the Culture, and the Nuance
Arabic is really a family of languages living under one name. Modern Standard Arabic is what you read in the news or a textbook, but it's rarely how people actually talk to each other. In the UAE, day-to-day conversation, humor, negotiation, and storytelling happen in Emirati Arabic, a Gulf dialect with its own vocabu…
Hugging FacePublished Oct 6, 2026
Cross-checked · Hugging Face BlogVerified Oct 8, 2026
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Jun Kim, oMLX creator and maintainer, joins Hugging Face to support the MLX community
MLX is Apple's framework for local AI, especially optimized for Apple Silicon. We are big MLX supporters since it was the Christmas present from Awni and Angelos in 2023, and proud that HF Mirror is the Hub where people find MLX models and contribute their own. Usage of open, local AI is accelerating, and we believe i…
Hugging FacePublished Sep 22, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
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One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO
Our recent results show that Nemotron is a strong, adaptable foundation for building world-class specialist models. Starting from Nemotron 3, our teams used supervised fine-tuning (SFT), reinforcement learning (RL), and feedback-driven inference to create systems that reached gold-medal level at both IMO 2026 and IOI…
Hugging FacePublished Oct 7, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
AI updateAI
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 rather than quietly broad…
Hugging FacePublished Oct 2, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
AI updateAI
Multimodal open d1 decision models for the edge
How we built decision models for the edge These open d1 decision models are built on our Liquid Foundation Models (LFMs). Unlike our generative models, decision models don’t produce tokens but answer in a single forward pass.
Hugging FacePublished Oct 8, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
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The model that didn't exist, so you made it yourself
How I prompt ML Intern 1. A model that knows your field 2. A model that draws your character 3. A model that does a new trick 4. A model that fits your device What it cost Make yours Last week, I wanted a small version of the prompt rewriter that ships with Qwen-Image 2.1. The official one is a 9B model that needs abo…
Hugging FacePublished Oct 8, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
AI updateAI
Rebuilding AUTOMATIC1111 with Gradio Workflow
Workflow1111 is a graph of eleven media pipelines built using seventy-three nodes . It brings together SOTA models for text-to-image, hi-resolution fix, image-to-image, prompt-matrix grids, VLM interrogate, detection-to-inpaint masks, ControlNet-style annotators, background removal, PNG Info storing, and image-to-vide…
Hugging FacePublished Sep 10, 2026
Cross-checked · Hugging Face BlogVerified Oct 2, 2026
AI updateAI
How UK AISI and EvalEval Are Making Benchmark Results Reproducible
AISI and EvalEval have previously collaborated on research that began at a joint workshop alongside NeurIPS 2025 , and feedback from the Institute has helped shape the Every Eval Ever (EEE) schema . This next phase of the collaboration puts that shared infrastructure into practice.
Hugging FacePublished Sep 22, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
AI updateAI
Real-Time Intelligence with IBM Time Series Models on Confluent
Until now, those decisions have run on outdated economics: one bespoke model at a time and months of expert work on each. So teams model the few hundred series where the money is and cover the rest with safety margins, extra inventory, extra headroom, extra tolerance, acted on after the window has closed. That margin…
Hugging FacePublished Sep 2, 2026
Cross-checked · Hugging Face BlogVerified Sep 10, 2026
AI updateAI
Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps
Structured output is one of the most common real-world tasks for LLMs, yet most benchmarks fold it into broader reasoning or extraction scores rather than measuring it on its own. Whether a model reliably returns valid, parseable output in the requested format and shape — schema compliance — is often what decides whet…
Hugging FacePublished Sep 3, 2026
Cross-checked · Hugging Face BlogVerified Sep 29, 2026
AI updateAI
Measuring benchmark optimization in speech recognition
One reason is that traditional benchmarks overlook many of the conditions and qualities that make voice systems reliable, natural, contextually appropriate, and effective in practice. That's why we recently introduced held-out sets in Real World VoiceEQ , the Open-ASR Leaderboard , and the Far-field ASR Leaderboard :…
Hugging FacePublished Aug 21, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic
Real deployments rarely fit the topic-level picture. The same base model may be adapted for a general assistant, an educational product, an enterprise system, or a public-sector service, and each setting needs different boundaries within the same topic. A civics tutor and a public-sector assistant can share a model ye…
Hugging FacePublished Sep 8, 2026
Cross-checked · Hugging Face BlogVerified Sep 22, 2026
AI updateAI
Up to 3.2x Faster Inference with LFM2.5-DSpark
How does DSpark work The decode phase in LLM inference is traditionally memory-bound. Most latency comes from streaming weights from DRAM into SRAM, not from intense computation. Speculative decoding addresses this by using a lightweight draft model to produce candidate tokens, then having the target model verify them…
Hugging FacePublished Aug 21, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
Meta is back with Muse Glimmer: local, agentic, multimodal, and open source
To celebrate, we are shipping with Meta day-0 support in transformers , llama.cpp , vLLM , Inference Endpoints, and other libraries. We built a few cool things and explain our findings in this blog.
Hugging FacePublished Aug 10, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
Training a coding model to paint watercolours with TRL and OpenEnv
On 23 August, Surya Narreddi posted a beautiful video of watercolours painted by a language model. The model writes JavaScript through p5.brush , a library that "adds natural drawing tools to p5.js". The video went viral fast, over 1.5M views at the time of writing.
Hugging FacePublished Sep 3, 2026
Cross-checked · Hugging Face BlogVerified Sep 24, 2026
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BenchMIRT: What are LLM benchmarks actually measuring?
Today we’re introducing BenchMIRT, a new method for auditing LLM benchmarks at the level of individual prompts—the questions and tasks a model is scored on.
Hugging FacePublished Sep 2, 2026
Cross-checked · Hugging Face BlogVerified Sep 23, 2026
AI updateAI
Making Knowledge Distillation Cheap Enough to Run at Scale
Knowledge distillation , training a smaller student model to match the performance of a larger teacher, is a well-known technique in Machine Learning. With the recent wave of open-source Large Language Models, such as gpt-oss , Qwen , GLM , or Kimi , it has become a mainstream research topic again. Deploying these ver…
Hugging FacePublished Aug 10, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
Quantization-Aware Healing: a compressed, 4-bit model that outperforms its full-precision original
Our latest paper, Quantization-Aware Healing: A Practical Recipe for Recovering Compressed, 4-Bit LLMs , asks a question that the field has mostly left open: once a model has already been through structural compression, not just quantization, how well does that recovery step actually work, and what is the right way to…
Hugging FacePublished Aug 25, 2026
Cross-checked · Hugging Face BlogVerified Sep 11, 2026
AI updateAI
Give Your Coding Agents a Memory You Own
Earlier this year, Software Forgets: Agent Traces Are the Memory made the case that coding agents already produce the record we keep losing. As they search a codebase, try approaches, hit errors, read documentation, and change direction, they leave behind a dense account of not just what changed, but why .
Hugging FacePublished Sep 3, 2026
Cross-checked · Hugging Face BlogVerified Sep 25, 2026
AI updateAI
Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS
By the time a user hears your application respond, you've already spent precious milliseconds capturing audio, transcribing speech, running an LLM, retrieving context, and generating a response. Text-to-speech (TTS) is the final step — and the one users notice most. If speech generation is slow, the whole experience f…
Hugging FacePublished Aug 11, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
Thinking of ACE? We Can Do It with Fewer Tokens
Give an LLM agent a realistic multi-step task — split a bill, find a song, reconcile an order across nine simulated apps — and when it fails, it usually isn't for lack of knowledge. It mis-paginates an API, resolves the wrong person, or returns a value when none was asked for. The model knows the APIs; what it hasn't…
Hugging FacePublished Aug 11, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI
Today, we are releasing the first layer of that effort: @huggingface/kernels , a minimal library for loading and running optimized WebGPU kernels from the HF Mirror Hub, together with an initial collection of 207 kernels at hf-mirror.com/webgpu-kernels .
Hugging FacePublished Sep 1, 2026
Cross-checked · Hugging Face BlogVerified Sep 23, 2026
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Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis
OlmoEarth Studio , our platform for building Earth observation models, now lets you compute and export embedding vectors —compact numerical representations of Earth-observation data produced by our open source OlmoEarth foundation models. The source code and model weights are publicly available alongside the research…
Hugging FacePublished Aug 13, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
Where a regular embedding model compresses a whole text into one vector, a multi-vector model keeps one vector per token and scores query against document with the MaxSim operator. That preserves token-level matching information that a single vector has to average away, which usually means stronger retrieval at the co…
Hugging FacePublished Aug 18, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics
World foundation models offer a different path. Instead of manually authoring every object and physical interaction, they learn visual dynamics directly from synchronized video and robot kinematics. NVIDIA's Cosmos-H-Surgical-Simulator demonstrated this approach by generating future surgical video from an initial scen…
Hugging FacePublished Jul 27, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
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Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets
You have an agent that can already record a demonstration and push it to the Hugging Face Hub . Now you want to run that loop continuously: collect episodes through the day, train a policy on the growing dataset, deploy it, and pull the next batch back to improve it. Run that loop once and every piece works. Run it ev…
Hugging FacePublished Aug 14, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
What We Learned by Reproducing 2,200 papers from ICML
In this post, we're sharing what we learned from running this hackathon, and what it suggests about the role humans will play when agents are doing the research experiments.
Hugging FacePublished Aug 13, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
NeoMME: an efficient Multimodal-native and Multilingual Encoder
TL;DR We introduce NeoMME , a family of 260M and 800M multilingual multimodal encoders. Unlike many generative visual language models, NeoMME does not use a separate pretrained vision tower or a causal language model. A single bidirectional Transformer processes both text tokens and raw image patches, and we train the…
Hugging FacePublished Sep 3, 2026
Cross-checked · Hugging Face BlogVerified Oct 1, 2026
AI updateAI
IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license
Time-series foundation models are changing the way forecasting systems are built. Instead of training and maintaining a separate model for every dataset, users can use a pretrained model and generate forecasts zero-shot.
Hugging FacePublished Sep 9, 2026
Cross-checked · Hugging Face BlogVerified Sep 16, 2026
AI updateAI
The Open ASR Leaderboard Adds Its First Global South Language
Benchmarks decide what gets built. A model that scores well on the Open ASR Leaderboard gets adopted and iterated on, while capabilities the leaderboard does not measure tend not to improve. Much of the recent work on the leaderboard has gone into making the evaluation metrics more trustworthy:
Hugging FacePublished Aug 28, 2026
Cross-checked · Hugging Face BlogVerified Sep 22, 2026
AI updateAI
Same Cluster, 33 Points More Utilization: What Changed Was the Order
We built a constraint-aware GPU allocator and benchmarked it against a FIFO scheduler across seven benchmark scenarios. On identical hardware, running identical workloads, GPU utilization rose by as much as 33 percentage points, and priority-weighted output rose in every one of them, by as much as 105%. Nothing about…
Hugging FacePublished Aug 18, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers
Finetuning multi-vector models involves several components: the model itself, datasets, loss functions, training arguments, evaluators, and the trainer class. I'll have a look at each of these components, accompanied by practical examples of how they can be used for finetuning strong multi-vector models.
Hugging FacePublished Aug 26, 2026
Cross-checked · Hugging Face BlogVerified Sep 22, 2026
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Wire It, Run It, Deploy It: AI Workflows in Gradio
gr.Workflow , built right into Gradio, makes the pipeline the interface . You describe your steps as a graph of typed nodes, and Gradio serves a drag-and-drop canvas where every node is runnable and every intermediate result is visible. The same graph is also a REST API and a one-command deploy to HF Mirror Spaces.
Hugging FacePublished Aug 25, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
How Much Memory Does Your Agent Actually Need?
Equipping an agent with agentic memory sounds simple: distill lessons from its past work, put them back in context, and more experience should mean better performance. It doesn't always work that way. When we scaled the evaluation to eight models — from a 30B dense model to frontier proprietary systems — one finding s…
Hugging FacePublished Aug 19, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
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Baseten on Hugging Face Inference Providers 🔥
We're thrilled to share that Baseten is now a supported Inference Provider on the HF Mirror Hub!
Hugging FacePublished Aug 6, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
GPU Management: Why Idle GPUs Are the New Grounded Aircraft
The reason is structural. An aircraft's costs accrue by the calendar hour: financing, depreciation, hull insurance, scheduled maintenance, crew contracts. Its revenue accrues only by the flight hour. Every hour spent on the ground shrinks the output side of that equation while the cost side keeps running exactly as be…
Hugging FacePublished Jul 30, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
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How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code
Of course, making AI research accessible requires a powerful search engine, so that humans and agents can quickly find relevant and related work, either through the website or the pwc search CLI command , which agents can use via the Skill .
Hugging FacePublished Aug 21, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
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Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem
Our latest paper, LLM Compression by Block Removal with Constrained Binary Optimization , takes that correspondence literally. We reformulate block selection as a constrained binary optimization (CBO) problem that maps directly onto an Ising glass , a disordered spin system with all-to-all interactions and a fixed num…
Hugging FacePublished Sep 21, 2026
Cross-checked · Hugging Face BlogVerified Sep 30, 2026
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Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident
We are publishing this level of detail because the technique matters more than the incident, as it reveals the emerging attack capabilities of the frontier agents, how they could be used by rogue actors, and how everyone should be prepared as defenders.
Hugging FacePublished Jul 27, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
AI updateAI
State of Open Models: Summer 2026 Observations
Models and datasets on HF hub are growing on a daily basis. Public model repositories grew from 2.43 to 2.96 million over the period, datasets from 711,000 to 1 million, Spaces from 1.00 to 1.44 million. The distribution underneath stays extreme, roughly 85.6% of models have fewer than 200 lifetime downloads, and 1.5%…
Hugging FacePublished Aug 14, 2026
Cross-checked · Hugging Face BlogVerified Sep 9, 2026
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Granite 4.2 LLMs: How They're Built
TL;DR: Granite 4.2 is our first family of dense, decoder-only reasoning LLMs, released in three sizes: 3B, 8B, and 30B . Each model is pre-trained from scratch on roughly 15T tokens with a five-phase strategy that extends the context window to 512K tokens, supervised fine-tuned on chain-of-thought, reasoning, and agen…
Hugging FacePublished Aug 25, 2026
Cross-checked · Hugging Face BlogVerified Sep 2, 2026
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Model Routing Is Simple. Until It Isn’t.
Except it’s not. Most routing systems assume that model selection is a classification problem. In our experience building routing into agentic systems, what looks like a model-selection problem quickly becomes a systems optimization problem. Three dimensions made this surprisingly hard for us.
Hugging FacePublished Jul 16, 2026
Cross-checked · Hugging Face BlogVerified Sep 3, 2026
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Profiling in PyTorch (Part 3): Attention is all you profile
This is the third post of Profiling in PyTorch, a series where we slowly build the skill of reading profiler traces and use it to drive optimization:
Hugging FacePublished Jul 10, 2026
Cross-checked · Hugging Face BlogVerified Aug 29, 2026
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Introducing Real World VoiceEQ: Measuring the human quality of voice AI
Voice is rapidly becoming AI's primary interface. From customer support and healthcare to education, entertainment, and personal assistants, speech is increasingly replacing text as the way people interact with AI.
Hugging FacePublished Jul 15, 2026
Cross-checked · Hugging Face BlogVerified Sep 2, 2026
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Grabette: an open system to record robot-manipulation data
The bottleneck isn’t the model. It’s the data. Robot learning has a supply problem. We have capable policy architectures (transformer-based VLAs, diffusion and flow-matching policies, and even world models) and the GPUs to train them. What we lack is large, diverse, real-world manipulation data.
Hugging FacePublished Jul 21, 2026
Cross-checked · Hugging Face BlogVerified Sep 4, 2026
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Welcome Inkling by Thinking Machines
Inkling is a large (1T params!) open model to natively accept image, text, and audio inputs.
Hugging FacePublished Jul 15, 2026
Cross-checked · Hugging Face BlogVerified Sep 2, 2026
AI updateAI
Newer Models, Same Advantage
The training pipeline was built in two stages. The first was a supervised fine-tuning step, drawing on a broad collection of Portuguese-language files from different sources, formats, and levels of complexity. This stage aligned the model's weights to the specific vocabulary, syntax, and document structures of Brazili…
Hugging FacePublished Jul 16, 2026
Cross-checked · Hugging Face BlogVerified Sep 4, 2026
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Security incident disclosure — July 2026
We identified unauthorized access to a limited set of internal datasets and to several credentials used by our services. We are still completing our assessment of whether any partner or customer data was affected, and we will contact any affected parties directly as required. We have found no evidence of tampering wit…
Hugging FacePublished Jul 16, 2026
Cross-checked · Hugging Face BlogVerified Sep 4, 2026
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Bringing Nunchaku 4-bit Diffusion Inference to Diffusers
Most of these backends are weight-only . This means that they store the weights in low precision and dequantize them back to high precision at compute time. This reduces memory usage significantly, but it usually does not make inference faster, and can even add a small latency overhead.
Hugging FacePublished Jul 23, 2026
Cross-checked · Hugging Face BlogVerified Sep 4, 2026