dinov3-vitl16-pretrain-lvd1689m-GGUF
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Explore AI models 18
GEM-X-GGUF
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View details ↗Kimodo-G1-RP-v1-GGML
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View details ↗Kimodo-G1-SEED-v1-GGML
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View details ↗Kimodo-SOMA-RP-v1.1-GGML
LocalAI-ioNative F32 GGML/GGUF conversion of nvidia/Kimodo-SOMA-RP-v1.1. The model predicts the compact SOMA 30-joint control skeleton. Its reusable Llama-derived text encoder is distributed separately as `Llama-3-Kimodo-GGML`. The model is installed at `models/kimodo-soma-rp-v1.1-f32.gguf` by `scripts/download_gguf_weights.sh --output "$PWD" --model soma-rp-v1.1`. Converted from upstream revision `6c9233af1180b8151e3c4703477104af5dce9dd5`. `MANIFEST.json` records the source revision and SHA-256 of the GGUF. The model remains subject to the NVIDIA Open Model License. This conversion grants no additional rights.
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View details ↗Kimodo-SOMA-SEED-v1.1-GGML
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View details ↗Llama-3-Kimodo-GGML
LocalAI-ioNative GGML/GGUF text encoder used by Kimodo. This is the reusable LLM2Vec encoder only; download a matching Kimodo diffusion model separately, for example `Kimodo-SMPLX-RP-v1-GGML`. From a kimodo.cpp checkout with the Hugging Face CLI installed, install both with: The downloader installs the recommended Q8_0 variant by default. Select a different one with `--text-quantization bf16|q8_0|q6_k|q5_k|q4_k|q4_k_m`. Every variant is one monolithic weight GGUF and uses the same `tokenizer.gguf`. Kimodo reads individual tensor ranges from the monolith, so streaming one layer at a time does not require separate per-layer files.
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View details ↗This model was converted to GGUF format from `mudler/LocalAI-functioncall-qwen2.5-7b-v0.5` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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View details ↗LocalVQE
LocalAI-io**Local Voice Quality Enhancement** — compact neural models for acoustic echo cancellation (AEC), noise suppression (NS), and dereverberation of 16 kHz speech, running on commodity CPUs in real time. Causal and streaming (256-sample hop, 16 ms latency). This page hosts the published weights. Inference runs the GGML C++ engine on the GGUF files directly (build instructions on GitHub). **Authors:** Richard Palethorpe (richiejp) and Claude (Anthropic). LocalVQE is a streaming, CPU-tuned derivative of **DeepVQE** (Indenbom et al., Interspeech 2023).
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View details ↗MotionBricks-G1-GGML
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View details ↗privacy-filter-GGUF
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View details ↗privacy-filter-multilingual-GGUF
LocalAI-ioGGUF conversion of `OpenMed/privacy-filter-multilingual`, a multilingual PII **token-classification** model (a fine-tune of `openai/privacy-filter`). It labels every token with a BIOES tag over **54 PII categories (217 classes)** across **16 languages**, so it can be served locally with **no Python** as the encoder/NER tier of a PII redactor. For the full model description, label space, evaluation, limitations, and citations, see the **source model card** — this card only covers the GGUF packaging and how to run it. This GGUF uses a **custom architecture, `openai-privacy-filter`**, that is not (yet) part of upstream llama.cpp. It runs on:
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View details ↗privacy-filter-nemotron-GGUF
LocalAI-ioGGUF conversion of `OpenMed/privacy-filter-nemotron`, a **fine-grained** PII **token-classification** model — a fine-tune of `openai/privacy-filter` on the `nvidia/Nemotron-PII` dataset. It labels every token with a BIOES tag over **55 PII categories (221 classes)** in a single forward pass, then decodes coherent spans with a constrained Viterbi procedure — so it can be served locally with **no Python** as the encoder/NER tier of a PII redactor. Where the base `openai/privacy-filter` covers 8 coarse categories, this fine-tune trades multilingual breadth for **category depth**: 55 fine-grained English categories (first/last name, government IDs, financial, healthcare, vehicle, digital, …). For the full model description, label space, evaluation, limitations, and citations, see the **source model card** — this card only covers the GGUF packaging and how to run it.
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View details ↗Qwen-Image-2.1-Anime-Fusal-GGUF
LocalAI-io**Built with Qwen.** This repository packages a tested LocalAI and stable-diffusion.cpp runtime for MicksHF/Qwen-Image-2.1-Anime-Fusal. It is a multi-file runtime assembly, not a merged checkpoint. The source model includes NSFW training material and is not for all audiences. This repository grants no rights to third-party artwork, characters, trademarks, datasets, or generated outputs. Users are responsible for lawful and responsible use. The text encoder does not include vision weights. This package supports text-to-image. It does not support reference-image editing without a compatible Qwen3-VL vision projector or combined encoder.
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View details ↗sam-3d-body-dinov3-GGUF
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View details ↗SkinTokens-GGUF
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View details ↗TRELLIS.2-4B-GGUF
LocalAI-ioGGUF (`f16`) conversions of microsoft/TRELLIS.2-4B for the **trellis2cpp** / ggml runtime — a CUDA-free, PyTorch-free C++ port of the TRELLIS.2 image-to-3D pipeline. Load these directly; no safetensors conversion or Python is needed at inference. The image conditioning encoder (DINOv3) and the sparse-structure decoder are published separately: dinov3-vitl16-pretrain-lvd1689m-GGUF and TRELLIS-image-large-GGUF. Converted from the upstream `bf16`/`fp16` safetensors to GGUF `f16` (ggml ftype 1) with the `convert_*_to_gguf.py` scripts in the trellis2cpp project. No weights were retrained or modified — this is a format conversion only.
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View details ↗TRELLIS-image-large-GGUF
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