☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. > **Compatibility warning:** AliceAI-T5 is a base encoder-decoder model, not > an instruction/chat model. It requires an AliceAI-aware llama.cpp runtime > with encoder, cross-attention, and decoder support. Stock `llama-cli`, > Ollama, vLLM, and LM Studio runtimes have not been validated for this > architecture. The runtime used for this release is derived from the > XHToken/llama.cpp fork. 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 length.
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View details ↗☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ 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 768-dimensional vectors in local `llama-server` CPU smoke checks. See the official model card for supported task prefixes, modalities, and input guidance. Every value below comes from local measurements of the locked BF16 GGUF and quantized files; the numbers are not copied from the upstream model card. The fixed task evaluation uses the 1,379-pair test split of MTEB STSBenchmark STS, dataset revision `96943a16ea6a35129e253c659081cb59daf81b30`. It covers 2,552 unique sentences with the `task: sentence similarity | query:` prefix, an 8,192-token context, and the CPU `llama.cpp` runtime at commit `9c2e0e491a822adae1f0b1c831adb4160057d24f`. Spearman and Pearson measure correlation between cosine similarity and human scores. Mean and 5th-percentile cosine measure vector agreement with the BF16 GGUF reference. Higher values indicate stronger task correlation or closer vector agreement; smaller files use less disk. This is a task-specific embedding evaluation, not the full MTEB benchmark. Token-level KLD, next-token Top-1, PPL, and RMS probability deltas do not apply to this embedding model.
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View details ↗☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ 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 validation documented below. The model uses the GLM-4.7 Flash chat template and XML-style tool calls. See the official upstream model card for the original model, training, and evaluation details.
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View details ↗☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ 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 `prompt_format.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 the observed cgroup estimate (process RSS plus charged file cache), in GiB. The BF16 row links to the upstream reference and was not quantized locally.
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View details ↗☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. K2-Horizon-MoVA-36B-A4B is a sparse Mixture-of-Experts model with Mixture-of-Values attention (MoVA). The upstream checkpoint has 36B total parameters and activates about 4B parameters per token. It advertises a native 524,288-token context window and is intended for reasoning, coding, agentic, and long-context workloads. See the official model card for the original model, serving recipes, prompt conventions, and upstream results. *Upstream K2-Horizon-MoVA-36B-A4B benchmark results; the image and scores are from the official model card.*
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View details ↗☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. 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, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, and Polish. It describes the source model as a reasoning model with tool use. This release's runtime checks cover short text generation; long-context and tool-call behavior have not been evaluated here. This release targets llama.cpp and compatible GGUF runtimes. See the official model card for the model's capabilities and usage guidance.
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View details ↗☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ 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 GGUF release keeps the vision projector in F16 and quantizes the language-model weights. The default upstream 16x visual-token compression is used.
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View details ↗Community GGUF quantizations of TokenRhythm/NeoHorse-1-4B. ☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. NeoHorse-1-4B is an approximately 4B-parameter text-only causal language model from TokenRhythm, post-trained from Qwen3.5-4B for text-based agent harnesses, tool use, coding, instruction following, and conversational use. The upstream card reports a native context length of 262,144 tokens; practical context depends on runtime, memory, KV-cache settings, and workload.
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View details ↗☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. Spark-X2.5-4B is a compact, general-purpose language model developed by SparkLLM. According to the upstream authors, the model is designed for: The upstream model is also designed for **broad hardware compatibility** and **efficient long-context inference**.
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View details ↗☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ 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 files; this package does not claim equivalent scores for these files. The package includes the upstream F16 multimodal projector. The ten quantizations from the initial release passed text generation smoke tests. Nine additional formats have also passed the same check; all 19 quantizations are published here. A held-out fidelity comparison to the locked F16 source has not been completed, so this card makes no fidelity ranking or quality percentage claim. Image input did not pass smoke validation with the pinned llama.cpp build: image encoding completed, but token decoding failed on both CUDA and CPU profiles. Treat image input as unverified with this runtime/package combination.
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View details ↗☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. *Frontier song quality and text alignment on 192 WildSongBench prompts. YuE2 uses symbolic planning; Bo8 means best-of-8.* This is the upstream YuE2 figure, reproduced for context and linked to the original model card. It is not a fidelity measurement of these GGUF files.
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View details ↗☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. ZDTaichu5.0-9B is a multimodal foundation model for visual understanding, spatial reasoning, agentic tool use, and embodied-AI workloads. The upstream model combines a Qwen3.5-9B language backbone with the C-RADIOv4-H vision encoder and accepts text, one or more images, and video. The upstream card describes any-resolution visual input and up to 128K tokens of context; the GGUF metadata preserves the converted model context configuration, while practical context length depends on the runtime and available memory. This release is for llama.cpp. The language model is provided as quantized GGUF files and the visual encoder / projector is provided separately as `mmproj-ZDTaichu5.0-9B-BF16.gguf`. Use the same mmproj file with each language-model quant when passing images or video. The official upstream model card and Transformers/vLLM instructions are available here.
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