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AI Models 2119

• MobileCLIP-S2-OpenCLIP↗

  apple

  MobileCLIP was introduced in MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training (CVPR 2024), by Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel. * Our smallest variant `MobileCLIP-S0` obtains similar zero-shot performance as OpenAI's ViT-B/16 model while being 4.8x faster and 2.8x smaller. * `MobileCLIP-S2` obtains better avg zero-shot performance than SigLIP's ViT-B/16 model while being 2.3x faster and 2.1x smaller, and trained with 3x less seen samples. * `MobileCLIP-B`(LT) attains zero-shot ImageNet performance of **77.2%** which is significantly better than recent works like DFN and SigLIP with similar architectures or even OpenAI's ViT-L/14@336.

• gpt2-demo↗

  demo-leaderboard

  GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.

• cotracker3↗

  facebook

  **CoTracker3** is a fast transformer-based model that was introduced in CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos. It can track any point in a video and brings to tracking some of the benefits of Optical Flow. You could read more about the paper on our webpage. Code is available here. - **Any pixel** in a video - A **quasi-dense** set of pixels together - Points can be manually selected or sampled on a grid in any video frame Online processing is more memory-efficient and allows for the processing of longer videos or videos in real-time.

• Bee-8B-RL↗

  Open-Bee

  We introduce **Bee-8B**, a new state-of-the-art, fully open 8B Multimodal Large Language Model (MLLM) designed to close the performance gap with proprietary models by focusing on data quality. Bee-8B is trained on our new **Honey-Data-15M** corpus, a high-quality supervised fine-tuning (SFT) dataset of approximately 15 million samples. This dataset was meticulously created with our transparent, adaptable, and open-source data curation pipeline, **HoneyPipe**, which systematically cleans noisy data and enriches it with a novel dual-level (short and long) Chain-of-Thought (CoT) strategy. This dataset enables Bee-8B to achieve exceptional performance, particularly in complex reasoning, establishing a new standard for fully open MLLMs.

• japanese-hubert-base↗

  yky-h

  This is a mirror of japanese-hubert-base, originally released by rinna Co., Ltd. The original model is licensed under the Apache License 2.0. This mirror follows the same license terms. All copyrights remain with the original authors. The model architecture is the same as the original HuBERT Base model, which contains 12 transformer layers with 12 attention heads. The model was trained using code from the official repository, and the detailed training configuration can be found in the same repository and the original paper. The model was trained on approximately 19,000 hours of following Japanese speech corpus ReazonSpeech v1. - ReazonSpeech

• Smaug-Llama-3-70B-Instruct-32K↗

  abacusai

  This is a 32K version of Smaug-Llama-3-70B-Instruct. It uses PoSE (https://arxiv.org/abs/2309.10400) and LoRA (https://arxiv.org/abs/2106.09685) adapter transfer. More details are coming soon. Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge)). GPT-4o and Gemini-1.5-pro-latest were missing from the original blob post, and we produced those numbers from a local run using the same methodology. Note that we believe the number of tokens/verbosity of the model strongly influences the GPT-4 judge in this case, and at least partially explains the improvement in Arena-Hard score for the 32K model.

• FastVideo-FastH3-8-Step-V2-NVFP4-Comfy↗

  FastVideo

  Original model repository: https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2 FastH3 V2 with its transformer quantized to NVFP4, for NVIDIA Blackwell GPUs: RTX 50 series, RTX PRO 6000 and DGX Spark. It is built from `fastvideo_fasth3_8step_v2_pruned_bf16.safetensors` in FastVideo/FastVideo-FastH3-Comfy, which also has the BF16 and INT8 builds for other GPUs. Use the **FastVideo FastH3: Text to Video** template (ComfyUI 0.36.0 or later) and select `fastvideo_fasth3_8step_v2_pruned_nvfp4.safetensors` (13.6 GB) in its `UNETLoader`. Keep the template's sampler settings: 8 steps, `res_multistep`, `simple`, and the `MiniMaxH3SigmaShift` node at 10 (video) and 3 (audio).

• SDAR-8B-Chat↗

  JetLM

  **SDAR** (**S**ynergy of **D**iffusion and **A**uto**R**egression) model is a new large language model that integrates autoregressive (AR) and discrete diffusion modeling strategies. It combines the efficient training paradigm of AR models with the highly parallel inference capability of diffusion models, while delivering performance fully on par with SOTA open-source AR models. At the same time, SDAR sets a new benchmark as the most powerful diffusion language model to date. We highlight three major conclusions from our study: > [!IMPORTANT] > Take-home message > > - **Balanced Efficiency:** SDAR unifies the **efficient training** of AR models with the **parallel inference** of diffusion, achieving both fast training and inference. > - **Fair Comparisons:** In rigorously controlled experiments, SDAR achieves **on-par general task performance** with strong AR baselines, ensuring credibility and reproducibility. > - **Superior Learning Efficiency:** On complex scientific reasoning tasks (e.g., GPQA, ChemBench, Physics), SDAR shows **clear gains over AR models** of the same scale, approaching or even exceeding leading closed-source systems. For **SDAR** models, inference hyperparameters are set to: `block_length = 4`, `denoising_steps = 4`, greedy decoding.

• sam2.1-hiera-base-plus-ONNX↗

  onnx-community

• nfnet_l0.ra2_in1k↗

  timm

  Normalization Free Networks are (pre-activation) ResNet-like models without any normalization layers. Instead of Batch Normalization or alternatives, they use Scaled Weight Standardization and specifically placed scalar gains in residual path and at non-linearities based on signal propagation analysis. Lightweight NFNets are `timm` specific variants that reduce the SE and bottleneck ratio from 0.5 -> 0.25 (reducing widths) and use a smaller group size while maintaining the same depth. SiLU activations used instead of GELU.