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AI Models 2119

  • functiongemma-270m-it↗

    unsloth

    > [!Note] > FunctionGemma is intended to be fine-tuned for your specific function-calling task, including multi-turn use cases. FunctionGemma is a lightweight, open model from Google, built as a foundation for creating your own specialized function calling models. FunctionGemma is not intended for use as a direct dialogue model, and is designed to be highly performant after further fine-tuning, as is typical of models this size. Built on the Gemma 3 270M model and with the same research and technology used to create the Gemini models, FunctionGemma has been trained specifically for function calling. The model has the same architecture as Gemma 3, but uses a different chat format. The model is well suited for text-only function calling. The uniquely small size makes it possible to deploy in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Furthermore, akin to the base Gemma 270M, the model has been optimized to be extremely versatile, performant on a variety of hardware in single turn scenarios, but should be finetuned on single turn or multiturn task specific data to achieve best accuracy in specific domains. To demonstrate how specializing the 270M parameter model can achieve high performance on specific agentic workflows, we have highlighted two use cases in the Google AI Edge Gallery app. The following is a code example of how to use FunctionGemma to generate a function call from a JSON definition using the Hugging Face Transformers library.

  • LTX-2.5-FP8↗

    unsloth

    This is an FP8 / INT8 quantized version of the LTX-2.5 distilled video DiT. - Each file replaces the 38.0 GB bf16 `transformer` of Lightricks/LTX-2.5-Diffusers with a 19.1 to 25.1 GB one. The text encoder, connectors, VAEs, vocoder and latent upsampler are unchanged and come from the Diffusers repo. - Same-seed LPIPS vs the bf16 DiT, averaged over all 121 frames of 4 clips (lower is better): 0.265 INT8-ConvRot, 0.366 FP8, 0.385 INT8. INT8-ConvRot is the recommended file. - FP8 and INT8 are bit-identical to quantizing the bf16 DiT at load with the same recipe, so they add no error over on-the-fly quantization; they save the download and the bf16 staging. Built from the `transformer/` of `Lightricks/LTX-2.5-Diffusers` at revision `da0b690960026eab794b8ceaf841233cf7064478` (tensor-identical to `ltx-2.5-22b-distilled-transformer-bf16.safetensors` in Lightricks/LTX-2.5). FP8 quantizes every linear with at least 512 features. INT8 keeps the audio stream, the audio-video cross attention, the adaLN and timestep projections and the norms in bf16. INT8-ConvRot rotates the inputs of all 480 quantized video linears with a block Hadamard (group 256) before quantizing; the rotation is listed in the file's metadata and applied to the activations at run time. Scores are against the bf16 DiT at the reference distilled settings (1280x704, 121 frames, 24 fps, 8 + 3 two-stage steps, CFG 1), text-to-video and image-to-video, seeds 42 and 7, with the same bf16 text encoder in every run; a repeated bf16 render is bit-identical (LPIPS 0.000). The distilled sampler injects noise every step, so small weight errors move the motion more than the look of a frame: every file keeps the subject, scene and prompt, as the samples show.

  • flux-3-image-klein↗

    drawthingsai

    FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. This repository holds the distilled checkpoint for generation and image editing. For fine-tuning, LoRA training and research, use FLUX 3 Image Klein Base. Inference code, prompt upsampling and the bounding box format: black-forest-labs/flux-3-image-klein on GitHub.

  • HRM-Text-1B↗

    sapientinc

    A 1 B-parameter language model checkpoint built on the **Hierarchical Reasoning Model (HRM)** architecture, trained by Sapient Intelligence from scratch on structured public datasets. HRM is a dual-timescale recurrent architecture: two Transformer modules (H = high-level / slow, L = low-level / fast) iterate over the same input embeddings for `H_cycles × (L_cycles + 1)` steps, with additive state injection (`z_L + z_H`). This gives effectively unbounded compute depth at bounded parameter count. This is a **pre-alignment** model checkpoint, not a chat or instruction-following assistant. It is pre-trained on a PrefixLM objective with condition prefix tokens and has **not** been multi-turn dialogue tuned, long-context adapted, instruction-tuned, RLHF-trained, or otherwise aligned for assistant-style use. If you want to use HRM-Text like a chat model, you would need to perform further alignment, such as SFT and/or RL, on task-specific data. This checkpoint is meant to serve as a starting point, not a finished assistant.

  • resnet50.fb_swsl_ig1b_ft_in1k↗

    timm

    This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Pretrained on Instagram-1B hashtags dataset using semi-weakly supervised learning and fine-tuned on ImageNet-1k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 25.6 - GMACs: 4.1 - Activations (M): 11.1 - Image size: 224 x 224 - **Papers:** - Billion-scale semi-supervised learning for image classification: https://arxiv.org/abs/1905.00546 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/facebookresearch/semi-supervised-ImageNet1K-models

  • coreml-sam2.1-tiny↗

    apple

  • maskformer-swin-tiny-coco↗

    facebook

    MaskFormer model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. Disclaimer: The team releasing MaskFormer did not write a model card for this model so this model card has been written by the Hugging Face team. MaskFormer addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation.

  • GLM-5.3-Flash-MXFP4↗

    OneNexus

    An MXFP4 GLM-5.3-Flash checkpoint for text and image-to-text serving on AMD Instinct MI355X (gfx950). On matched text-reasoning samples against amd/GLM-5.3-Flash-Quark-MXFP4, it scores **+1.0 percentage point on MMLU** and **+3.5 points on GPQA Diamond**, with **−0.2 point on GSM8K**. These are observed differences on fixed samples, not statistically established superiority or a claim about vision, safety, or general agentic quality. Runtime support: SGLang PR #39273. Both checkpoints answered the same fixed questions with one greedy attempt per question. A capped or unfinished response counts as incorrect.

  • MiniMax-H3-360-Orbit-LoRA↗

    pablodawson

    **A LoRA for MiniMax-H3 (FL2VA variant) that turns one photo into a frozen-time 360° camera orbit, and lands back on the exact frame it started from.** Give the model the same image as the first and the last keyframe, and the LoRA orbits the camera all the way around the subject while the scene stays frozen. The clip closes on its own first frame, so orbits can be chained or stitched into longer shots without a visible seam. Each comparison shows three renders of the same input with the same seed, prompt, resolution and step count:

  • xglm-564M↗

    facebook

    XGLM-564M is a multilingual autoregressive language model (with 564 million parameters) trained on a balanced corpus of a diverse set of 30 languages totaling 500 billion sub-tokens. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin\*, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li\* (\*Equal Contribution). The original implementation was released in this repository. For intended usage of the model, please refer to the model card released by the XGLM-564M development team. The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi.