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#Hugging Face
All collected and published items, ordered by time. Picks are a selected subset.
Models
New models and versions, open weights, and changes in capability and pricing, with their evaluations.
Hugging Face · X13 Hugging Face BlogPicks83 Reflection releases Beam, a 501B open model for coding and agentic work
Reflection released Beam on Hugging Face, a 501B MoE model with 23B active parameters per token, a 1M-token context window, and an Apache 2.0 license. The text-only model targets coding and agentic work, with BF16, FP8, and NVFP4 checkpoints and day-0 support in transformers, vLLM, and SGLang. Hugging Face provides deployment and post-training recipes alongside its own task evaluations and demos.
Why it matters: The deployment guidance distinguishes active computation from total weight memory, helping teams assess hardware requirements for running and adapting a sparse model with a long context window.
Artificial Analysis · X63 Reflection’s Beam scores 30 on Artificial Analysis Intelligence Index, ranking #2 among US open weights models
Reflection’s first model, Beam, scores 30 on the Artificial Analysis Intelligence Index, placing it #2 among US open weights models. Beam is an open weights model with 501B total and 23B active parameters, released on Hugging Face.
NVIDIA AI · XPicks77 Reflection launches Beam, a 501B open-weight MoE model
Reflection launched Beam, a 501B MoE model, with open weights under the Apache 2.0 license and 40+ ecosystem partners. NVIDIA AI says it was trained on NVIDIA GB300 NVL72 and delivers over 70K tokens/sec/GPU on Blackwell Ultra out of the box on day 0. Reflection’s quoted announcement includes BF16, FP8 and NVFP4 weights, a DFlash2 drafter, and Mirror CLI; the Beam 1.0 NVFP4 checkpoint is available at https://huggingface.co/reflection/Beam-1.0-501B-A23B-NVFP4.
Why it matters: The Apache 2.0 license, multiple weight formats, and released training harness give developers concrete information for assessing Beam’s suitability for their deployment workflows.
Hugging Face BlogPicks74 Apertus 1.5 Release Notes Detail Multimodal Understanding, Thinking Mode, and Longer Context
Project Apertus released Apertus 1.5 in 8B and 70B sizes, adding image and audio understanding, optional thinking, a 262,144-token context window, and improved instruction-following and tool use. The weights were released on Hugging Face on July 24, 2026 under Apache 2.0; the models generate only text, and spoken-language processing remains experimental. The team also publishes training data, recipes, intermediate checkpoints, and reproducible evaluation records.
Why it matters: Published intermediate checkpoints, training mixtures, and evaluation settings provide a concrete basis for investigating how individual training stages change model behavior and for reproducing reported results.
Artificial Analysis · XPicks78 Fish Audio’s Drama 3 Preview leads open-weights models in Artificial Analysis TTS rankings
Artificial Analysis reports that Fish Audio’s Drama 3 Preview ranks #1 among open-weights models on all three of its Text to Speech leaderboards and leads all models in Pronunciation Robustness. It scores 93.2% on pronunciation robustness, ahead of Eleven v4 at 91.7% and Eleven v4 Turbo at 90.1%, while ranking #10 in Provider Voice and #14 in Controlled Voice. Its weights are available on Hugging Face under the Fish Audio Community License, and Fish Audio API pricing is $15/1M characters.
Why it matters: The comparison separates pronunciation robustness from voice preference rankings, showing how Drama 3 Preview’s lead on one measure differs from its placement on others.
Hugging Face BlogPicks79 Liquid AI releases LFM2.5-24B-A2B for fast, private on-device agents
Liquid AI released LFM2.5-24B-A2B, an open-weight reasoning model with 24B total parameters and 2B active parameters, for on-device agents that reason and call tools. Its context window expands from 32K to 131K tokens, and both the reasoning and Base versions are available on Hugging Face. Liquid AI reports 184 tokens/s on an Apple M5 Max and competitive tool-use results, while acknowledging that larger models retain advantages on knowledge-heavy and coding benchmarks.
Why it matters: The benchmark comparison helps distinguish workloads suited to local deployment, with strong tool use and instruction following alongside weaker knowledge and coding results than some larger models.
vLLM · X62 vLLM Congratulates vllm-sr on Open-Sourcing Decision 3.0 Multimodal Model
vLLM congratulated the vllm-sr team on releasing Decision 3.0, described as a state-of-the-art multimodal Jev model whose weights are now open-sourced on Hugging Face. The quoted announcement from Xunzhuo Liu says the open model family spans 0.6B to 27B and ranks #1 on both Jev Decision Index boards in its evaluation, on a new Pareto frontier.
Qwen · XPicks65 Qwen releases Qwen-Image-2.1-Turbo, generating and editing images with 8-step denoising and open weights
Qwen has released Qwen-Image-2.1-Turbo, based on the same 7B visual generation architecture as Qwen-Image-2.1, requiring only 8 denoising steps to generate and edit images, with weights now open on ModelScope and Hugging Face.
Why it matters: Qwen's image model gains a Turbo accelerated version, with 8-step sampling and open weights offering a generation and editing path that can be integrated directly.
vLLM · X40 vLLM Semantic Router team ships Decision 2.0 for single-pass routing and classification
Great work from the vLLM Semantic Router team on Decision 2.0. Multiple questions about one input, answered in a single forward pass with per-option probabilities. Useful for routing and classification. Quoted post by @XunzhuoLiu: In every size. Decision 2.0: six open decision models, 0.6B to 27B. On the #Jev Decision Index 0.3, each one tops the same-size open models we lined it up against. #Trending collections on Hugging Face 🤗 Decision 2.0: https://huggingface.co/collections/vllm-sr/decision-20 Vela 2.0: https://huggingface.co/collections/vllm-sr/vela-20
Hugging Face Blog40 TII launches Falcon-ASR, a 1.6B-parameter Arabic speech recognition model focused on Emirati dialects
Abu Dhabi's Technology Innovation Institute (TII) released Falcon-ASR, a 1.6B-parameter speech recognition model focused on Arabic and Emirati dialects, with support for English, French, Spanish, and Portuguese as well. Its average WER across six Arabic test sets is 20.92%, 2.25 percentage points below the best public result of 23.17% in the leaderboard snapshot used. In an internal Emirati dialect evaluation, it achieved a 22.73% WER and 10.19% CER, outperforming Qwen3-Omni.
Hugging Face BlogPicks62 Liquid AI open-sources d1-3B and d1-omni-600M on-device decision models
Liquid AI released two open-weight models in its d1 decision-model family: d1-3B and the experimental d1-omni-600M.
Why it matters: Liquid AI open-sourced two decision models and reported on-device latency and results across seven benchmarks, providing evidence on the use of small models for structured decisions.
Perplexity · X58 Perplexity releases pplx-embed-v2-late multimodal embedding models
Perplexity is releasing pplx-embed-v2-late, described as two late-interaction embedding models that retrieve text, images, and pages through a shared embedding space for cross-modal querying. The company says both models reach frontier performance and are publicly available on Hugging Face, with a blog post at perplexity.ai/hub/blog/multimodal-embeddings-beyond-a-single-vector.
The Decoder66 Google releases EmbeddingGemma 2 multimodal embedding model
Google released EmbeddingGemma 2, an open multimodal embedding model that converts text, images, video, audio, and code into numerical vectors. It has 740 million parameters. Google says it outperforms competitors with more than twice as many parameters on multimodal embedding benchmarks and scores 78.68 on the Massive Text Embedding Benchmark (Code), nearly 10 points above the previous model's 68.76.
Sundar Pichai · X62 Google releases EmbeddingGemma 2, an open multimodal embedding model
Sundar Pichai announced EmbeddingGemma 2, described as Google's first open, natively multimodal embedding model. It handles text, code, image, video, and audio tasks in a modular 740M parameter form factor, is positioned for offline, privacy-first RAG when paired with Gemma 4, and is said to outperform some specialist models more than twice its size. Weights are available now on Hugging Face.
Hugging Face Blog26 Falcon-Emirati-7B: teaching LLMs the Emirati dialect, culture, and feel for language
Falcon-Emirati-7B is a specialized model for Emirati dialects built on Falcon-H1-Arabic. It aims to understand and generate Emirati Arabic like a native speaker. It uses the Falcon-H1 hybrid architecture, running Mamba alongside Transformer attention. The base model family comes in 3B, 7B, and 34B sizes, with a context window of up to 256K tokens.
AWS Machine Learning BlogPicks62 GLM 5.3 launches on Amazon Bedrock
Zhipu AI (Z.ai)'s GLM 5.3 is now available on Amazon Bedrock. The 753B-parameter mixture-of-experts model targets coding and long-horizon agent tasks and is offered through a fully managed API with cross-region inference, prompt caching, and service tiers. It is currently available to eligible enterprise customers.
Why it matters: The source describes how to access GLM 5.3 on Bedrock and use prompt caching, helping readers assess the cost impact of managed inference for coding and long-horizon agent workflows.
The Decoder62 Aleph Alpha releases Kolibri, an open-weight 78B model
Aleph Alpha released Kolibri, a bilingual German-English model with 78B total parameters using a mixture-of-experts architecture that activates about 3B parameters per token. The company says Kolibri sits on the Pareto frontier for quality and operating cost in both languages. It scores 71% on German benchmarks and decodes faster than comparable models including GPT OSS A5B, Qwen 3.6 A3B, and Gemma 4 A4B.
The Decoder38 NASA and IBM release open-source lunar foundation model based on 17 years of LRO observations
NASA and IBM Research, together with several academic institutions, released the NASA-IBM Lunar Foundation Model, which they describe as one of the first open-source foundation models for lunar science.
The Decoder66 Cloudflare releases Clef and Clef-flash decision models, saying agent decisions no longer need human involvement
Cloudflare released Clef and Clef-flash, two decision models for AI agents. They return brief classifications with probabilities rather than long text, and the company says agent decisions no longer need human involvement.
Hugging Face BlogPicks62 Ai2 open-sources AstaBrief 8B, a scientific report-generation model
Ai2 open-sourced AstaBrief 8B, a model that turns research questions and retrieved literature excerpts into reports with citations. It was trained from Qwen3-8B using SFT and DPO and is now available as Asta's Fast mode.
Why it matters: The source describes AstaBrief's training-data construction and citation-filtering details, which can be adapted to deploy other long-form generation models.
Hugging Face BlogPicks62 AI2 releases Olmo-core 3, open and scalable training infrastructure for large MoE models
AI2 released Olmo-core 3, a major upgrade to its large language model development framework. It redesigns the open MoE training system to scale MoE training to trillions of parameters while maintaining compute efficiency.
Why it matters: The source provides throughput and memory comparisons for the MoE training stack, helping readers assess changes in the cost of large-scale MoE training.
Hugging Face BlogPicks62 NVIDIA releases Kumo Tabular, a foundation model for tabular prediction
NVIDIA has released Kumo Tabular, an open-source tabular foundation model that predicts labels for new rows in a single forward pass from labeled table rows, supporting classification and regression without training, tuning, or feature engineering. The model comes in three sizes from 28M to 215M parameters, was pretrained entirely on synthetic tables, is commercially usable under the OpenMDW-1.1 license, and ranks first on four benchmarks: TabArena, BeyondArena, TALENT, and ScoringBench.
Why it matters: The source reports model sizes, training-data provenance, and rankings on four benchmarks, helping readers assess the accuracy and efficiency tradeoffs of tabular foundation models.
量子位56 Zhizhi Innovation Institute releases IQuest-Q1, with 320B total and 15B active parameters
IQuest Research at Zhizhi Innovation Institute has released IQuest-Q1, a decoder-only Transformer with a sparse mixture-of-experts architecture, about 320B total parameters and about 15B active parameters. It is available on GitHub and Hugging Face.
Hugging Face Blog41 Liquid AI releases LFM2.5-VL-DSpark draft model to accelerate LFM2.5-VL-3B inference
Liquid AI has released the experimental DSpark draft model for its LFM2.5-VL-3B vision-language model. Speculative decoding speeds inference without changing output quality: up to 3.13× on-device and 2.66× on an H100, with end-to-end speedups of up to 2.62× and 2.27×, respectively.
Hugging Face Blog40 Hugging Face releases NeoMME, 260M and 800M multimodal multilingual encoders
Hugging Face has introduced the NeoMME family of multimodal multilingual encoders in 260M and 800M sizes. A single bidirectional Transformer directly processes text tokens and raw image patches, without a separate vision tower or causal language model.
Z.ai 智谱 · XPicks78 Zhipu releases open weights for GLM-5.3
Zhipu has announced open weights for GLM-5.3, which it calls its most capable model for agentic coding and cyber defense. The weights can now be downloaded, run, and customized from Hugging Face at zai-org/GLM-5.3; technical details are at z.ai/blog/glm-5.3.
Why it matters: Zhipu has opened the weights of its flagship model for agentic coding and cyber defense, giving readers a view of the options for self-hosting and customization.
Z.ai 智谱 · X62 Zhipu says GLM-5.3 weights will be released tomorrow
Zhipu's official account announced that GLM-5.3 weights will be released tomorrow and linked to the zai-org/GLM-5.3 repository on Hugging Face.
Hugging Face BlogPicks62 IBM releases Granite 4.2 reasoning models in 3B, 8B, and 30B sizes
IBM's Granite team has released Granite 4.2, its first family of dense decoder-only reasoning models, in 3B, 8B, and 30B sizes. They were post-trained from the Granite-4.1 base and are all licensed under Apache 2.0.
Why it matters: The article discloses Granite 4.2's architecture, SFT data mix, and multi-stage RL curriculum in detail, offering a look at concrete post-training methods for reasoning models.
Hugging Face Blog56 Liquid AI releases LFM2.5-DSpark draft models, accelerating inference by up to 3.2×
Liquid AI has released DSpark draft-model checkpoints for LFM2.5-1.2B-Instruct, LFM2.5-2.6B, and LFM2.5-8B-A1B. Speculative decoding increases decoding speed without changing output quality, with up to 3.18× higher GPU throughput and up to 2.87× speedup on-device.
Hugging Face Blog34 Hugging Face OlmoEarth Studio adds custom embedding export
Hugging Face's OlmoEarth Studio can now compute and export embeddings; its code and model weights are public. Through the UI or API, users can select a region, time range, encoder variant (Nano: 128 dimensions and 1.4M parameters; Tiny: 192 dimensions and 6.2M parameters; Base: 768 dimensions and 89M parameters), resolution, and imagery source, then export an int8 Cloud-Optimized GeoTIFF.
Hugging Face BlogPicks62 NVIDIA releases open-weights Magpie TTS multilingual model, adding Arabic, Korean, and Brazilian Portuguese
NVIDIA has released Magpie TTS Multilingual, a 364M-parameter open-weights speech-synthesis model supporting 12 languages. The latest release adds Modern Standard Arabic, Korean, and Brazilian Portuguese, and improves quality in several existing languages through updated training data.
Why it matters: The article reports the 364M open TTS model's language expansion and measured TTFA on different GPUs, helping readers estimate latency budgets for self-hosted voice pipelines.
Hugging Face BlogPicks82 Meta releases open-source 30B multimodal Muse Glimmer for local agents
Meta has released Muse Glimmer, a 30B-parameter multimodal model distilled from Muse and licensed under Apache 2.0. It targets local-agent use cases such as coding, document analysis, and personal assistance.
Why it matters: Meta has open-sourced a 30B multimodal model with reproducible details on its architecture, benchmarks, local deployment, and self-improvement demonstrations.
Hugging Face BlogPicks88 Thinking Machines releases Inkling family of open multimodal models
Thinking Machines Lab has released the open-source Inkling family on Hugging Face. Inkling is an MoE model with about 1T total and 41B active parameters, natively supporting image, audio, and text inputs with a 1M-token context. The release also includes Inkling-Small, with 276B total and 12B active parameters.
Why it matters: The article gives Inkling's architecture, deployment setup, and benchmark comparisons, helping readers assess the practical requirements of an approximately 1T-parameter open multimodal model.
Mistral AIPicks71 Mistral releases Leanstral 1.5 formal-verification model
Mistral has released Leanstral 1.5, a formal-verification model licensed under Apache 2.0 with 119B total and 6B active parameters. It scores 100% on miniF2F, solves 587 of 672 PutnamBench problems, and sets state-of-the-art scores of 87% on FATE-H and 34% on FATE-X.
Why it matters: The source reports Leanstral 1.5's formal-verification benchmark results and open-source availability, helping readers assess its usefulness in proof engineering.
Google ResearchPicks76 Google releases TabFM, a zero-shot foundation model for tabular data
Google has released TabFM, a zero-shot foundation model for classification and regression on tabular data. It recasts table prediction as an in-context-learning problem, producing predictions in a single forward pass without manual training, tuning, or feature engineering.
Why it matters: The source details TabFM's architecture and TabArena results, helping readers assess whether zero-shot table prediction can replace traditional tree-model workflows.
Google DeepMindPicks71 Google DeepMind releases DiffusionGemma, generating text up to 4× faster
Google DeepMind has released DiffusionGemma, an experimental open-source model that uses text diffusion to generate entire chunks of text in parallel. It generates text up to 4× faster on specialized GPUs, exceeding 1,000 tokens per second on a single NVIDIA H100 and 700 tokens per second on an RTX 5090.
Why it matters: Google DeepMind's open text-diffusion model comes with concrete speed and hardware figures, helping readers assess the feasibility of local interactive inference.
Mistral AIPicks62 Mistral AI releases Voxtral TTS speech-synthesis model
Mistral AI has released Voxtral TTS, its first text-to-speech model. The 4B model supports nine languages—English, French, German, Spanish, Dutch, Portuguese, Italian, Hindi, and Arabic—and offers expressive emotion and low latency.
Why it matters: The source reports the 4B model's language coverage and 70 ms latency, and compares it with ElevenLabs, helping readers assess cost and usability for voice agents.
Mistral AIPicks72 Mistral releases Voxtral Transcribe 2 speech-to-text model family
Mistral has released Voxtral Transcribe 2, with two speech-to-text models: Voxtral Mini Transcribe V2 for batch transcription and Voxtral Realtime for live use.
Why it matters: The source reports latency, pricing, and open-source licensing for both transcription models, helping readers assess options for real-time speech workloads.
Mistral AIPicks78 Mistral releases Voxtral speech-understanding models in open 24B and 3B sizes
Mistral has released Voxtral speech-understanding models in 24B and 3B sizes, both open-source under Apache 2.0 and available through an API. They support a 32K-token context, can transcribe up to 30 minutes of audio or understand up to 40 minutes, and provide built-in question answering and summarization, automatic multilingual detection, and speech-triggered function calls. Their text capabilities are inherited from Mistral Small 3.1.
Why it matters: Mistral's open speech-understanding models come in two sizes, under Apache 2.0, with API pricing disclosed, helping readers assess transcription and audio-understanding costs.