Community GGUF quantizations of yandex/AliceAI-T5-35B-A0.6B. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. AliceAI-T5-35B-A0.6B is a Yandex encoder-decoder language model with sparse mixture-of-experts layers. The upstream model card describes approximately 34.35B unique parameters, 512 experts per MoE layer with top-8 routing, a 1,536-wide hidden state, 16 encoder layers, 12 decoder layers, shared encoder/decoder embeddings, and RoPE with YaRN. The upstream card advertises a 128K-token context window; this GGUF release has not been validated at that maximum context…
Open weights
apache-2.0
llama.cpp
Community GGUF quantizations of jialinyyzz/humanizer. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. Humanizer is an English and Chinese text-completion model for rewriting drafts. See the upstream model card for the model description and examples. Build the exact prompt as instr + "\n\n" + draft.strip() + sep and send it to /completion; the original promptformat.json is included here. Upstream notes that GGUF chat templates are available but untested by the authors. Sizes are measured from the local files in decimal GB (bytes / 1,000,000,000). Peak build RSS + cache is…
Open weights
apache-2.0
llama.cpp
Community GGUF quantizations of empero-ai/Qwen3.8-35B-A3B-Distill. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. This release is based on the upstream Qwen3.8-35B-A3B-Distill model card. The model is a text-generation MoE model with 35B total parameters and approximately 3B active parameters per token, using 256 experts with 8 routed experts. It uses hybrid attention components and has a native context length of 262,144 tokens in the upstream configuration. The GGUF files are intended for local inference with llama.cpp and compatible runtimes. The upstream model card…
Open weights
apache-2.0
llama.cpp
Community GGUF quantizations of whitecircle/GLM-4.7-Flash-Coder. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. GLM-4.7-Flash-Coder is an agentic coding fine-tune of zai-org/GLM-4.7-Flash. The upstream model card describes a 30B Mixture-of-Experts model trained on software-engineering trajectories, with tool use, reasoning, and concurrent tool-call behavior. The upstream training configuration uses a 75k-token sequence length; its model configuration declares a 202,752-token maximum position limit. This release does not claim long-context behavior beyond the runtime…
Open weights
mit
llama.cpp
Community GGUF quantizations of bottlecapai/ThinkingCap-Qwen3.8-27B. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. This is a community GGUF package of BottleCap AI's 27B dense vision-language model, based on Qwen3.8-27B. The upstream model card describes hybrid linear and full attention, image and video understanding, an MTP head, and a native context length of 262,144 tokens. See the upstream model card for its capabilities, evaluation results, and recommended sampling settings. Those upstream task measurements apply to the upstream checkpoint and its exact quantized…
Open weights
other
llama.cpp
Community GGUF quantizations of google/embeddinggemma-2. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. EmbeddingGemma 2 is a multilingual, multimodal embedding model from Google DeepMind. It maps text and code, images, video, and audio into a shared 768-dimensional vector space and has an 8,192-token shared context window. The upstream checkpoint has a 270M-parameter text path plus optional vision and audio encoders. This release contains a quantized text backbone and a shared BF16 vision/audio projector. Text, image, and audio inputs returned normalized…
Open weights
apache-2.0
llama.cpp
Community GGUF quantizations of LiquidAI/LFM2.5-24B-A2B. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. Modification notice: The GGUF files in this repository are community-modified derivatives of the upstream BF16 checkpoint, converted to GGUF and quantized by this publisher. LFM2.5-24B-A2B is Liquid AI's text-only reasoning Mixture-of-Experts model. It has 24 billion total parameters and 2.45 billion active parameters, with 40 layers, 64 experts per MoE block, top-4 routing, and a 131,072-token context window. The upstream card lists English, Arabic, Chinese, French…
Open weights
other
llama.cpp
Community GGUF quantizations of openbmb/MiniCPM-V-4.7-1B. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. MiniCPM-V 4.7 1B is OpenBMB's compact multimodal model for image and video understanding. The upstream model describes single-image, multi-image, and video use, with an efficient vision encoder and on-device deployment targets. See the official model card and the upstream deployment guide. The upstream configuration declares a 262,144-token text context. The example below uses a 4,096-token context; long-context behavior has not been evaluated in this release. This…
Open weights
apache-2.0
llama.cpp
