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AI Models 2119

  • MythoMax-L2-Kimiko-v2-13B-GGUF↗

    TheBloke

    GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

  • gte-base-en-v1.5↗

    Alibaba-NLP

    We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance. The models are built upon the `transformer++` encoder backbone (BERT + RoPE + GLU). The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to Evaluation). We also present the `gte-Qwen1.5-7B-instruct`, a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.

  • Ingot-VIII-27B-Q2↗

    dendriteholdings

    A public mirror of a model that won on Ingot, a Bittensor subnet where miners quantize a fixed teacher model and the validator crowns the copy that stays closest to it. It is Prism ML's Ternary Bonsai 2 27B, refined: the same ternary codes, with the per-block scales and the norm weights re-tuned by the miner to bring it closer to Qwen3.8-27B. The weights are the miner's upload, byte for byte. Credit belongs to the miner, and to Prism ML for Bonsai 2.

  • SambaLingo-Hungarian-Chat-70B-i1-GGUF↗

    mradermacher

  • openjev↗

    openjev

    **Typed decisions about text, web pages and screenshots. One forward pass per question with up to 52 options. About 210 ms for an isolated web decision on one H100, with roughly 1,460 prompt tokens and 23 options.** OpenJev is an open-weights **decision model**. You describe the decision in plain words at request time, with your own labels, and it answers with a choice, a yes / no probability or a score. The same model routes a support ticket, flags a policy violation, judges whether an answer is grounded, and tells a browser agent which button to press next. Three questions in one API request, three typed answers, each with a probability for every option. One forward pass per question (up to 52 options).

  • BgGPT-Gemma-3-27B-IT↗

    INSAIT-Institute

    BgGPT 3.0 is a series of Bulgarian-adapted LLMs based on Gemma 3, developed by INSAIT. Available in 4B, 12B and 27B sizes. 1. **Vision-language understanding** — The models understand both text and images within the same context. 2. **Instruction-following** — Trained on a broader range of tasks, multi-turn conversations, complex instructions, and system prompts. 3. **Longer context** — Effective context of 131k tokens for longer conversations and complex instructions. 4. **Updated knowledge cut-off** — Pretraining data up to May 2025, instruction fine-tuning up to October 2025. Load the model in FP8 at runtime for ~2x memory reduction with minimal quality loss — no separate quantized checkpoint needed:

  • MiniMax-H3-curve-GGUF↗

    joeygambino

    GGUF quantizations of MiniMax-H3's 33B video+audio DiTs, built from MiniMax's **pruned** checkpoints. Same model, same quant tiers as the original-form repo, **about 40% smaller** - and a Q8_0 that fits a 24 GB card. `fl2va` = text/first-last-frame to video+audio (T2V and I2V). `ref2va` = reference-conditioned generation (identity from up to 9 images, 3 videos with soundtracks, 3 voice clips). Runs in ComfyUI with ComfyUI-GGUF plus a one-line architecture patch (node pack: **ComfyUI-H3-Multishot**; workflows: **MiniMax-H3-Multishot-Workflow**).

  • gemma-4-12B-it-qat-OptiQ-4bit↗

    mlx-community

    > **Built with mlx-optiq**, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. Try the Lab · All OptiQ quants · Docs A 4-bit mixed-precision MLX quant produced by mlx-optiq, built on Google's quantization-aware-trained (QAT) Gemma-4 base. OptiQ's sensitivity-guided per-layer bit allocation is applied on top of weights that were trained to survive low-bit quantization, and it still beats a uniform 4-bit quant of the same QAT base by +1.37 Capability Score points. This is a quant of google/gemma-4-12B-it-qat-q4_0-unquantized. Per-layer bit-widths come from a KL-divergence sensitivity pass on a six-domain calibration mix (prose, reasoning, code, agent, tool-call, constraint-bearing instructions). Sensitive layers go to 8-bit, robust ones stay at 4-bit.

  • PP-LCNet_x1_0_table_cls↗

    PaddlePaddle

    The Table Classification Module is a key component in computer vision systems, responsible for classifying input table images. The performance of this module directly affects the accuracy and efficiency of the entire table recognition process. The Table Classification Module typically receives table images as input and, using deep learning algorithms, classifies them into predefined categories based on the characteristics and content of the images, such as wired and wireless tables. The classification results from the Table Classification Module serve as output for use in table recognition pipelines. The key metrics are as follow: Model Top1 Acc(%) GPU Inference Time (ms) [Regular Mode / High-Performance Mode] CPU Inference Time (ms) [Regular Mode / High-Performance Mode] Model Storage Size (M) You can also integrate the model inference of the table classification module into your project. Before running the following code, please download the sample image to your local machine.

  • Unlimited-OCR-GGUF↗

    sahilchachra

    GGUF quantizations of **baidu/Unlimited-OCR**, a 3B vision-language OCR model that pushes **DeepSeek-OCR** one step further (one-shot, long-horizon document parsing). This repo contains a full spread of **K-quants and i-quants** of the language model plus the **vision projector (mmproj)** needed for image input. > ⚠️ **Requires a DeepSeek-OCR–aware llama.cpp build (PR #17400).** > Unlimited-OCR uses the DeepSeek-OCR architecture (a SAM+CLIP *DeepEncoder* vision tower > with a DeepSeek-V2 MoE text decoder). Support is **not yet merged into upstream `main`** — > stock llama.cpp will not load these files. Build the PR branch (instructions below). Every run needs **two** files: one language model GGUF (pick a quant) **plus** the shared vision projector. The projector is fp16 and identical for all quants.