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AI Models 2119

• Index-Echo-S2TT-GGUF↗

  audio-cpp

  GGUF packages for native speech-to-text translation with audio.cpp. Chinese speech produces timestamped source transcripts and English, Spanish, or Japanese translations. This port supports **S2TT only**; it does not generate translated speech. **There is room to improve 9B output parity and quantized quality**. Focused PRs with reproducible C++/Python comparisons are welcome. Note that 2B and 9B use the same C++ translation path, including the prompt, audio encoder, decoder implementation, and greedy sampler. 2B orig is parity safe, while 9B orig has drifts. 2B has 24 layers and tied token/output embeddings; 9B has 32 layers and a separate LM head. That can change how sensitive their outputs are to numerical differences, but I have not proved that it explains the 9B mismatch. These files were converted from the official 2B checkpoint at `72fd8bdc9bf8251f1c35f6d6289a950eda154395` and 9B checkpoint at `05f86cb7a38684916e193282b6cc02462ace4043`. Each GGUF embeds the audio tower, connector, text decoder, tokenizer, configuration, and audio.cpp model spec. The unused vision weights are not included.

• wide_resnet50_2.tv2_in1k↗

  timm

  This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample

• qwen2.5-7b-instruct-arabic-yt-merged↗

  HarithSami

• eomt-dinov3-ade-semantic-large-512↗

  tue-mps

  This is the **large** variant of the EoMT-DINOv3 model trained for **semantic segmentation** on ADE20K at **512×512** resolution. **EoMT (Encoder-only Mask Transformer)** is a Vision Transformer (ViT) architecture designed for high-quality and efficient image segmentation. It was introduced in the CVPR 2025 highlight paper: **Your ViT is Secretly an Image Segmentation Model** > **Key Insight**: Given sufficient scale and pretraining, a plain ViT along with a few additional parameters can perform segmentation without the need for task-specific decoders or pixel fusion modules. The same model backbone supports semantic, instance, and panoptic segmentation with different post-processing.

• sd-vae-ft-mse-flax↗

  enterprise-explorers

  These weights are intended to be used with the 🧨 diffusers library. If you are looking for the model to use with the original CompVis Stable Diffusion codebase, come here. We publish two kl-f8 autoencoder versions, finetuned from the original kl-f8 autoencoder on a 1:1 ratio of LAION-Aesthetics and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces. The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS). The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU). To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder.

• GLM-5.3-NVFP4↗

  Inferact

  GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: + Stronger Coding: GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam. + Emergent Cyber Capability: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks. GLM-5.3 supports deployment with the following frameworks. Feel free to try them out:

• Qwen2.5-3B-CyberSecurity-Instruct↗

  Mohamedabul

  A fine-tuned version of `Qwen2.5-3B-Instruct` specifically designed for advanced cybersecurity analysis. This model is built to bridge the gap between high-level vulnerability descriptions and low-level exploit code execution. 1. **Vulnerability Triage**: Automatically generating structured severity, attack vector, and mitigation reports for any CVE. 2. **Exploit Reverse-Engineering**: Analyzing raw exploit code (C, Python, Bash) to provide an immediate technical breakdown of *how* the exploit works and what vulnerabilities it targets. 3. **Attack Chain Reasoning**: Combining a CVE with raw exploit code to generate a step-by-step kill-chain analysis, from initial access to system compromise. To achieve maximum accuracy, the model was fine-tuned on an expansive historical corpus of modern vulnerabilities and exploits, completely uncapped and unfiltered: - **NVD CVE Database**: Vulnerabilities published between 2020 through 2025. - **Exploit-DB**: Over 45,000+ real-world exploits directly from Offensive Security. - **MITRE CWE**: Full weakness classifications, likelihood of exploit, and abstractions. - **Total Dataset Size**: ~187,700 structured instruction samples.

• ByteCraft↗

  SamsungSAILMontreal

• Anima-2.9B↗

  Gazingstars123

  **Every bit of support helps expand the model's scope and capability even further!** You may still need to install ComfyUI-Anima-2.9B to the custom node folder if your comfyui are not up-to-date (version 0.33.1). Plug and play, there is no custom node needed. Sometimes may not work with other custom nodes Anima-2.9B is a fine-tune and layer-expansion of circlestone-labs/Anima. The base Anima model targets anime, illustration, and non-photorealistic art; this release continues training on that foundation with the expanded architecture. The model is trained on an additional 1.7M anime/illustration samples, with knowledge cutoff in July 2026, making Anima-2.9B one of the most up-to-date anime/illustration model at release.

• Z-Image-Turbo-FP8↗

  unsloth

  This is an FP8 / INT8 quantized version of Z-Image-Turbo. - Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.181 INT8, 0.130 FP8. ⚡️- Image An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer Z-Image is a powerful and highly efficient image generation model family with **6B** parameters. Currently there are four variants: