⚡ Each donation = another big MoE quantized I host 30+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. **APEX (Adaptive Precision for EXpert Models)** quantizations of InternScience/Agents-A1 — a 35B Mixture-of-Experts agentic model built to scale heterogeneous agentic abilities across long-horizon search, engineering, scientific research, instruction following, and tool calling. APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers (first/last 5) get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).
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⚡ Each donation = another big MoE quantized I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. **APEX (Adaptive Precision for EXpert Models)** quantizations of samuelcardillo/Carnice-Qwen3.6-MoE-35B-A3B. APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).
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⚡ Each donation = another big MoE quantized I host 30+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. **APEX (Adaptive Precision for EXpert Models)** quantizations of samuelcardillo/Carnice-Qwen3.6-MoE-35B-A3B, with the **MTP (multi-token prediction) head bundled** for in-the-box self-speculative decoding. These GGUFs bundle the model's **MTP (multi-token prediction) head** alongside the trunk in a single file, courtesy of llama.cpp PR #22673. With a recent llama.cpp (>= commit 255582687) you can enable self-speculative decoding using just this one file — no separate draft model needed:
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GGUF quantizations of the **CED** family (Consistent Ensemble Distillation, Xiaomi) - SOTA-tier audio-tagging models that classify everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark, ...) into the 527-class AudioSet ontology. These files run with **ced.cpp**, a standalone C++/ggml port (no Python, no PyTorch at inference), and with **LocalAI** via the `ced` backend. Converted from the `mispeech/ced-*` checkpoints (Apache-2.0). CED is a plain AST/DeiT Vision Transformer over a log-mel spectrogram; the port is numerically equal to the PyTorch reference. One self-contained GGUF per size + quant (config, 527 labels, and the mel filterbank/window are all embedded). Pick by your accuracy/size budget:
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GGUF conversions of ByteDance Depth Anything 3, for use with **depth-anything.cpp** — a from-scratch C++17 / ggml port. No Python, no PyTorch, no CUDA toolkit at inference: one self-contained GGUF file plus a small native library and CLI, **faster than PyTorch on CPU** and **bit-exact** against the original (correlation 1.0, verified component by component). Given an image, the engine recovers a dense **depth** map, per-pixel **confidence**, camera **extrinsics (3×4)** and **intrinsics (3×3)**, an optional **sky** mask, a back-projected **3D point cloud**, and exports to **glb / COLMAP / PLY**. Each GGUF is fully self-contained — every dimension, hyperparameter and preprocessing constant is baked into the file; the loader reads them, nothing is hardcoded.
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Each `.gguf` here is a self-contained, metadata-driven pack (detector + recognizer, plus genderage / anti-spoof heads when the source provides them) produced by face-detect.cpp, a standalone C++/ggml port of the insightface and OpenCV-Zoo face pipelines. No Python or ONNX runtime is needed at inference time: the GGUF carries the weights verbatim plus the forward-graph topology in its KV metadata, and the C++ engine replays it. The packs in this repo carry **two different licenses** depending on their source weights. Pick the pack that matches your use case. > **The insightface buffalo packs (SCRFD + ArcFace) are released by insightface for > NON-COMMERCIAL research purposes only.** They are redistributed here as derived > GGUF artifacts under those same upstream terms. If you need a commercial-friendly > option, use **`yunet-sface.gguf`** (Apache-2.0).
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⚡ Each donation = another big MoE quantized I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. Benchmarks coming soon (re-quantized with llama.cpp b8664 including Gemma 4 tokenizer and logit softcapping fixes). For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF. APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient -- edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).
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⚡ Each donation = another big MoE quantized I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. **APEX (Adaptive Precision for EXpert Models)** quantizations of gemma-4-26B-A4B-it-Claude-Opus-Distill — a Claude Opus reasoning-distilled version of google/gemma-4-26B-A4B-it by TeichAI. Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF.
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⚡ Each donation = another big MoE quantized I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. 🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors 💚 Big thanks to Hugging Face for generously donating additional storage, much appreciated.
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⚡ Each donation = another big MoE quantized I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. **APEX (Adaptive Precision for EXpert Models)** quantizations of Jackrong/Gemopus-4-26B-A4B-it-Preview. APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient -- edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).
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⚡ Each donation = another big MoE quantized I host 30+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. 🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors **APEX (Adaptive Precision for EXpert Models)** quantizations of Kwaipilot/KAT-Coder-V2.5-Dev — Kwaipilot's Mixture-of-Experts model for agentic coding.
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입력 텍스트→ 출력 텍스트
⚡ Each donation = another big MoE quantized I host 30+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. 🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors **APEX (Adaptive Precision for EXpert Models)** quantizations of poolside/Laguna-XS-2.1 — poolside's Laguna XS.2 Mixture-of-Experts model for coding and agentic software engineering.
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Perplexity is measured on wikitext-2-raw. KL divergence is measured against the BF16 model on the same text. HellaSwag and Winogrande use 400 tasks each, zero-shot. The LiquidAI rows are the quantizations published by LiquidAI, measured the same way. Perplexity differences of about 0.2 are close to the measurement error (+/- 0.14). APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient: edge layers get higher precision, middle layers get more aggressive compression. The I-variants use imatrix-guided quantization. APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.
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GGUF builds of `nvidia/LocateAnything-3B` for **locate-anything.cpp** - a C++/ggml inference engine for open-vocabulary detection / visual grounding, no Python at inference time. The detections are the same as the official PyTorch implementation (the engine is parity-gated against it), and it runs faster - on CPU and GPU. The full-precision `f32` GGUF (~15 GB) is reproducible from the HF weights with `scripts/convert_locateanything_to_gguf.py` in the repo.
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