#Open source/Repo

AIAlibaba-NLP's UEmbed-2B, a decoder-only multimodal embedding model built on Qwen3.5 2B, produces both dense and SPLADE-style sparse embeddings from a single forward pass. It supports text, image, video, and mixed-modal inputs for retrieval, and the 4B and 9B variants are also available. The team reports state-of-the-art results on the text and agent tracks of MMEB-v3.

AIMiniMax released H3, an open-weights omni-modal model that generates video with native stereo audio up to 2K and 15 seconds. The system combines H3-Context-IR preprocessing, the H3-Base generator at 768p, and H3-Regenerate-2K for 2K output, with the Context-IR and 2K modules available only through API.

Why it matters: The source details a three-module pipeline and open weights with deployment paths, showing how a video model is served and reproduced locally.

AILiquid AI released LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, bidirectional encoders built on the LFM2 hybrid architecture and available on Hugging Face. They support an 8,192-token context and are designed for fine-tuning on classification and token-level tasks. On CPU, LFM2.5-Encoder-230M is the fastest model tested from 1K tokens up, running about 3.7x faster than ModernBERT-base at 8,192 tokens.

AICohereLabs has published North-Mini-Code-1.0-eagle, an Eagle draft model for its North-Mini-Code-1.0 target model. The three-layer draft uses dense transformer blocks and sliding window attention, and it is compatible with fp8 and w4a16 target checkpoints in vLLM.

AIBAAI has released AREX-Base, a 122B-total, 10B-activated Mixture-of-Experts deep research agent built on Qwen3.5-122B-A10B with a 262,144-token context. The model uses an inner research loop and an outer self-improvement loop, and the source reports it scoring 82.5 on BrowseComp and 85.4 on GAIA, under Apache 2.0.

Why it matters: The release pairs a 122B-parameter deep research agent with benchmark tables against frontier and open models, letting readers compare its search-agent results directly.

AIBAAI's AREX-Turbo is a dense 4B deep research agent built on Qwen3.5-4B with a 262,144-token context length. It scores 70.7 on BrowseComp, 81.6 on GAIA and 40.6 on HLE with tools, versus 82.5, 85.4 and 52.4 for the 122B AREX-Base. The model is released under Apache License 2.0 and targets lower-cost research-agent deployment.

AIMistral AI released Shieldstral-1.0-3B, a 3B-parameter multimodal safety classifier that judges content against natural-language policies and outputs a continuous safety score. It moderates text, image, and text-plus-image content in a single forward pass and can be retargeted to new policies at inference time without retraining. The Apache 2.0 open-weight model is built on Ministral-3-3B-Base-2512 and trained on sequences up to 32k tokens.

AILiquid AI released Antidoom, an open-source method that retrains a single overtrained token to eliminate "doom loops" in small reasoning models. On LFM2.5-2.6B and Qwen3.5-4B, loop rates fell from 10.2% to 1.4% and from 22.9% to 1%, respectively. The company also released IFStruct, an open-source benchmark measuring whether model outputs satisfy a schema, where LFM2.5-350M rose from 21.10% to 44.90% after training.

AIByteDance's UniVR-34B-Planning, built on Emu3.5 at 34B parameters, learns visual reasoning, physical dynamics, and long-term planning from visual demonstrations using a next-token objective and two-stage training on the VR-X dataset with VR-GRPO reinforcement learning. On the VR-X benchmark it scores 58.2 overall, up 18.4 points from the Emu3.5 34B baseline of 39.8. The Planning checkpoint is available on Hugging Face under CC BY 4.0, alongside a General checkpoint.

AITufa Labs won the first $37.5K ARC-AGI-3 milestone prize with "The Duck," a small open-source LLM that plays the games by writing and running Python in a live REPL. Reki placed second with a vision-language agent using Gemma-4-31B, and md Boktiar Mahbub Murad placed third with the "forge" framework. The second and final milestone prize ends September 30.

AIA model repository named XiaomiMiMo/MiMo-V2.5-DFlash is listed on Hugging Face with 311B parameters and tensor types F32, BF16, and F8_E4M3. The README is empty, and the page reports 434 downloads last month and no Inference Provider deployment.

AIDeepSeek has posted the eagle3_gemma4_12b_ttt7 model on Hugging Face, listed at 2B parameters in BF16 tensor format with 621 downloads last month. The model card is empty and no Inference Provider currently deploys it, while it appears in the DeepSpec collection of 12 items.

AIDeepSeek published a model named eagle3_qwen3_14b_ttt7 on Hugging Face, listed at 2B parameters in BF16 with the Safetensors format. The model has no model card, is not deployed by any Inference Provider, and recorded 400 downloads last month.

AIDeepSeek has published eagle3_qwen3_8b_ttt7 on Hugging Face, a model listed at 2B parameters in BF16 safetensors format. The model card is missing, it has 577 downloads last month, and no Inference Provider currently deploys it.

AIDeepSeek has published eagle3_qwen3_4b_ttt7 on Hugging Face, a 0.9B-parameter model in BF16 tensor format. The model has no model card and is not deployed by any Inference Provider, and it is listed in the DeepSpec Collection.

AIQwen released Qwen3-ForcedAligner-0.6B-hf, a Transformers-format forced aligner that predicts timestamps for arbitrary units within up to 5 minutes of speech in 11 languages. The model accepts transcripts from any ASR system, and the documentation shows it paired with Qwen3-ASR-0.6B and NVIDIA Parakeet CTC. Until it ships in an official Transformers release, users must install Transformers from source.

AICohere and Cohere Labs released Cohere Transcribe Arabic, an open-source 2B-parameter Arabic automatic speech recognition model under Apache 2.0. It is optimized for Arabic, Arabic dialects, English, and Arabic-English code-switched speech, using a Conformer encoder-decoder architecture supported natively in Transformers. The model's average WER of 25.87 and CER of 11.80 on the Open Universal Arabic ASR Leaderboard, as of 07.07.2026, is reported in the source.

AIZ.ai has released GLM-5.2, its flagship model for long-horizon tasks, which it says substantially improves on GLM-5.1 and supports a 1M-token context. The model adds IndexShare, which cuts per-token FLOPs by 2.9× at 1M context, and is released under the MIT open-source license.

Why it matters: The source gives benchmark tables against named rival models and deployment settings, useful for judging where GLM-5.2 sits among current flagship models.

AIMoonshot AI released Kimi K3 on Hugging Face as an open-weight, native multimodal agentic model with 2.8T total parameters and 104B activated parameters. It supports a 1-million-token context window and text and image input, with weights released under the Kimi K3 License. The model card reports benchmark results for coding, agentic, and vision tasks against several closed models, and recommends vLLM, SGLang, or TokenSpeed for inference.

Why it matters: The release pairs open weights with a 2.8T-parameter MoE architecture and benchmark tables against several named closed models, useful for comparing frontier capability claims.

AIByteDance open-sourced inference code and model weights for Bernini-Diffusers, a full video generation and editing pipeline with an MLLM-based semantic planner and a DiT-based renderer. The release bundles a Qwen2.5-VL planner and Wan2.2 diffusion components in one self-contained directory, and the source recommends it over the renderer-only Bernini-R for complex instruction following.

AIEvoQuality is a ByteDance vision-language model for no-reference image quality assessment that generates pseudo-ranking labels through pairwise majority voting and refines them with GRPO, requiring no human-annotated quality scores. On the paper's setting, it raised weighted-average PLCC from 0.615 to 0.770 and SRCC from 0.570 to 0.726 over its Qwen2.5-VL-7B backbone. The model is recommended for research and pre-production assessment, not as the sole criterion for high-stakes decisions.

AIZ.ai released SCAIL-2, an open-source model that animates a reference character from a driving video without skeleton maps or inpainting masks. It also supports character replacement, multi-character scenes, and animal-driving, with 512p and 704p resolutions and inputs whose height and width are both divisible by 32.

AIByteDance has open-sourced the 1.3B-parameter weights of its Bernini Renderer (Bernini-R), available on Hugging Face as ByteDance/Bernini-R-1.3B-Diffusers. Fine-tuned from Wan2.1-1.3B, the model performs close to the 14B variant on simple tasks such as style transfer, subtitle or watermark removal, and local editing, but lags on complex tasks such as human generation. The release requires a CUDA GPU, with an H100 recommended for FlashAttention-3.

AIXiaomi MiMo has released MiMo-V2.5-Pro-FP4-DFlash, the FP4 backbone behind MiMo-V2.5-Pro-UltraSpeed, with MXFP4 quantization applied only to the MoE experts and a BF16 DFlash drafter for block-diffusion speculative decoding. The backbone has 1.02T total and 42B active parameters, and the drafter proposes blocks of up to 8 tokens per forward pass. The release is supported in SGLang, with example launch commands provided.

AIXiaomi and TileRT released MiMo-V2.5-Pro-UltraSpeed, reporting decode speeds above 1000 tokens/s on a 1-trillion-parameter model using a single standard 8-GPU node. The API is priced at 3x MiMo-V2.5-Pro and is available by application only from June 9 to June 23, 2026. The speedup relies on FP4 quantization of MoE Experts, DFlash speculative decoding with an average coding acceptance length of 6.30, and TileRT compute kernels.

Why it matters: The post traces how FP4 quantization, DFlash speculative decoding, and TileRT kernels combine to reach 1000+ tokens/s on a single 8-GPU node, which is useful for teams weighing inference throughput.

AICohere and Cohere Labs released North Mini Code 1.0, an open-weights 30B-A3B mixture-of-experts model for code generation and agentic terminal tasks, under Apache 2.0. The model has 256K context and 64K max output, and is trained for tool use. Its benchmark table lists Terminal-Bench v2 at 36.0, SWE-Bench Verified at 67.6, and LiveCodeBench v6 at 70.3, below Qwen3.6 on several tasks.

Why it matters: The card lists benchmark results against Qwen3.6, Gemma4, and other models, showing where North Mini Code trails on some coding and agentic tasks.

AIMiniMax has released MiniMax-M3, a native multimodal model with a 1M-token context window, roughly 428B total parameters, and about 23B activated parameters. The model introduces MiniMax Sparse Attention, which the source says delivers 9× prefill and 15× decode speedups over M2 at 1M context. M3 supports enabled, adaptive, and disabled reasoning modes through the thinking parameter, and weights are available on Hugging Face.

Why it matters: The source gives concrete attention-efficiency figures and three reasoning modes, which helps readers judge long-context cost against deployment choices.

AIByteDance has open-sourced the inference code and model weights of the Bernini Renderer (Bernini-R), a DiT-based renderer paired with an MLLM-based semantic planner for video generation and editing. A diffusers-format version, ByteDance/Bernini-R-Diffusers, bundles the Wan2.2 base components with the Bernini-R transformer weights for direct loading, and the framework requires a CUDA GPU with PyTorch 2.5.1+cu124.

AIStability AI released Stable Audio 3.0, a family of four audio models trained on fully licensed data. Three of them, Small SFX, Small and Medium, have open weights on Hugging Face, while Large is available through the Stability AI API and enterprise self-hosting. Outputs can be distributed and commercialized under the Stability AI Community License, and organizations with more than $1M in annual revenue can use the Enterprise License.

Why it matters: The source specifies which models are open-weight, their licensing terms, and clip-length limits, which matters for anyone deciding whether to build on them.

AIMistral AI has released mistralai/Mistral-Medium-3.5-128B-EAGLE, an EAGLE draft model for speculative decoding with the 128B dense Mistral Medium 3.5. The companion model, which the source says replaces Mistral Medium 3.1 and Magistral in Le Chat and Devstral 2 in Vibe, has a 256k context window, handles text and image input with text output, and is served with vLLM or SGLang using three speculative tokens. The model is released under a Modified MIT License that allows commercial use with exceptions for companies with large revenue.

AIXiaomi released and open-sourced MiMo-V2.5-Pro, a 1.02T-parameter Mixture-of-Experts model with 42B active parameters and a 1M-token context window. The company reports gains in agentic tasks, software engineering, and long-horizon work, including a Rust SysY compiler task finished in 4.3 hours across 672 tool calls. Weights and tokenizer are on Hugging Face, and API pricing is unchanged.

Why it matters: The release pairs a 1.02T-parameter open-weight model with long-horizon agent results and token-efficiency claims, useful for judging its fit in coding and agent workflows.

AIApple has released CADD-Base-7B on Hugging Face, a 7B masked diffusion language model for code generation that uses Continuously Augmented Discrete Diffusion (CADD) to guide discrete denoising with a continuous flow-matching signal. The model loads through Transformers with trust_remote_code, and its diffusion_generate method supports CADD sampling modes "weighted" and "argmax" with alg options such as "entropy" and "maskgit_plus". The release builds on DiffuCoder and reuses Dream's modeling architecture and generation utilities.

AIOpenAI is releasing a subset of datasets, reference code, and the g-mean 2 metric for evaluating chain-of-thought monitorability. The release includes most datasets from its monitorability suite, while some evaluations relying on private or restricted data were omitted. The company says it will keep reporting monitorability results in future frontier reasoning model system cards.

AIOpenAI released Privacy Filter, a bidirectional token-classification model that detects and masks personally identifiable information in text under the Apache 2.0 license. The model has 1.5B total parameters with 50M active, supports a 128,000-token context window, and can run in a web browser or on a laptop. Users can fine-tune it and adjust precision/recall tradeoffs through preset operating points.

AIBlack Forest Labs released FLUX.2 Small Decoder, a distilled VAE decoder that works as a drop-in replacement for the standard FLUX.2 decoder on Hugging Face. It decodes about 1.4x faster and uses about 1.4x less VRAM at decode time, with ~28M decoder parameters versus ~50M in the full decoder and minimal quality loss. It is available under the Apache 2.0 license and is compatible with FLUX.2-klein-4B, FLUX.2-klein-9B, FLUX.2-klein-9b-kv, and FLUX.2-dev.

AIPrismAudio is a framework that integrates reinforcement learning into video-to-audio generation, using a Chain-of-Thought planning mechanism. It builds on ThinkSound by splitting single-step reasoning into four CoT modules for semantic, temporal, aesthetic, and spatial dimensions, each with targeted reward functions. Code, model weights, and datasets are released for research and educational use under the MIT License, and commercial use requires explicit author authorization.

AIApple has released apple/SimpleSD-30B-instruct, a research checkpoint built on Qwen that uses Simple Self-Distillation to improve code generation without rewards, verifiers, or teacher models. On LiveCodeBench, the model scores 55.3% pass@1 on LCBv6 versus 42.4% for its base, Qwen3-30B-A3B-Instruct-2507. The checkpoints are for reproducibility, not optimized Qwen releases, and are available under the Apple Machine Learning Research Model License.

AIApple has published SimpleSD-4B-thinking on Hugging Face, a research checkpoint built on Qwen that improves code generation through Simple Self-Distillation without rewards, verifiers, teacher models, or reinforcement learning. On LiveCodeBench, it lifts Qwen3-4B-Thinking-2507 from 54.5% to 57.8% pass@1 on LCBv6 and from 59.6% to 63.1% pass@1 on LCBv5. The model is released as a reproducibility checkpoint under the Apple Machine Learning Research Model License, not as an optimized Qwen release.

AIBerkeley AI Research introduces SPEX, a signal-processing framework that identifies influential interactions in LLMs using far fewer ablations than exhaustive analysis. A hierarchy-based extension, ProxySPEX, matches SPEX performance with around 10x fewer ablations. The methods apply to feature, data, and model component attribution.

AIFun-CineForge, from FunAudioLLM, is an open-source toolkit with an end-to-end dataset pipeline and an MLLM-based model for zero-shot movie dubbing across diverse cinematic scenes. The team built CineDub-CN, described as the first large-scale Chinese television dubbing dataset, and reports that its model outperforms state-of-the-art methods on audio quality, lip-sync, timbre transition, and instruction following. Inference code and checkpoints were released on March 16, 2026, and the model runs on a consumer-grade GPU.