We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute.
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View details ↗local-inference-lab/Qwen3.8-Flash-Next-NVFP4 is a mixed-precision model distilled from Qwen/Qwen3.8-Flash-Next using quantization-aware distillation (QAD). The student is trained against the original BF16 teacher with quantized weights in its forward pass, learning to compensate for quantization error rather than relying on post-training quantization alone. The architecture is unchanged: 48 decoder layers, 512 routed experts per layer with 10 active per token, hybrid Gated DeltaNet/Qwen Sparse Attention (QSA), and n-gram embedding tables. Compression comes from lower-precision weights, not fewer layers or experts. The checkpoint occupies approximately 98 GiB on disk and is particularly suited to run on a single RTX 6000 (with PLE offload), or a DGX Spark (with or without PLE offload). NVFP4 stores 4-bit E2M1 values with FP8 block scales per 16 elements and an FP32 global scale. MXFP8 stores 8-bit E4M3 values with power-of-two block scales per 32 elements. Residual-mixing and PLE normalization weights are trained in FP32 and exported in BF16. Shared-expert scalar gates remain frozen in BF16.
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View details ↗Native 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 ↗Native 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 ↗**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 ↗GGUF 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 ↗GGUF 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 ↗**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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