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mellum2-12b-a2.5b-instruct-q8
Mellum2-12B-A2.5B-Instruct is an Apache-2.0 mixture-of-experts model from JetBrains with 12 billion total parameters, 2.5 billion activated per token, and a 131,072-token context window. This entry uses the higher-quality Q8_0 GGUF quantization.

Repository: localaiLicense: apache-2.0

kat-coder-v2.5-dev-q8
KAT-Coder-V2.5-Dev is an Apache-2.0 agentic coding model from Kwaipilot, post-trained from Qwen3.6-35B-A3B. It has 35 billion total parameters with 3 billion activated per token, a 262K-token context window, and text-only weights tuned for repository-level coding and tool use. This entry uses the higher-quality Q8_0 GGUF quantization.

Repository: localaiLicense: apache-2.0

laguna-s-2.1-q8
Laguna S 2.1 is Poolside's 118B-parameter, 8B-active Mixture-of-Experts model for agentic software engineering. It supports tool use and a native one-million-token context window; the official GGUF recommends 256K context for best output quality. This entry uses the 129 GB Q8_0 build, with routed experts quantized to Q8_0 and the signal path kept in BF16. License: OpenMDW 1.1.

Repository: localaiLicense: other

privacy-filter-nemotron-q8
Q8_0 quant of privacy-filter-nemotron (~1.64 GB, vs ~2.8 GB for F16) for RAM-constrained / edge use (e.g. a 4 GB Raspberry Pi 5). The MoE expert weights are stored 8-bit; attention, embeddings and the classifier head stay F16. Same model, policy and runtime as the F16 entry - see privacy-filter-nemotron for the full description. Prefer the F16 entry when you can afford it: it is the reference artifact. On a mixed-PII document the publisher measured q8 matching F16 on 99.93% of token labels with an identical span set at threshold 0.5 - but one token flipped, and for PII a single dropped span is a leak. Treat q8 as a deliberate size/speed tradeoff and validate it on your own data.

Repository: localaiLicense: apache-2.0

fara1.5-27b-q8
Fara1.5-27B is Microsoft's 27B-parameter multimodal computer-use agent for web browsers, fine-tuned from Qwen3.5-27B. It accepts screenshots and text, emits structured browser actions, supports a 262K-token context, and should be deployed with appropriate sandboxing and user-confirmation controls. This entry uses the Q8_0 GGUF quantization.

Repository: localaiLicense: mit

nanbeige4.1-3b-q8
Nanbeige4.1-3B is built upon Nanbeige4-3B-Base and represents an enhanced iteration of our previous reasoning model, Nanbeige4-3B-Thinking-2511, achieved through further post-training optimization with supervised fine-tuning (SFT) and reinforcement learning (RL). As a highly competitive open-source model at a small parameter scale, Nanbeige4.1-3B illustrates that compact models can simultaneously achieve robust reasoning, preference alignment, and effective agentic behaviors. Key features: Strong Reasoning: Capable of solving complex, multi-step problems through sustained and coherent reasoning within a single forward pass, reliably producing correct answers on benchmarks like LiveCodeBench-Pro, IMO-Answer-Bench, and AIME 2026 I. Robust Preference Alignment: Outperforms same-scale models (e.g., Qwen3-4B-2507, Nanbeige4-3B-2511) and larger models (e.g., Qwen3-30B-A3B, Qwen3-32B) on Arena-Hard-v2 and Multi-Challenge. Agentic Capability: First general small model to natively support deep-search tasks and sustain complex problem-solving with >500 rounds of tool invocations; excels in benchmarks like xBench-DeepSearch (75), Browse-Comp (39), and others.

Repository: localaiLicense: apache-2.0

nanbeige4.2-3b-q8
Nanbeige4.2-3B is a compact agentic and reasoning model with 3B non-embedding parameters. Its Looped Transformer architecture reuses transformer layers to increase model capacity without adding parameters. It targets tool use, coding, mathematics, scientific reasoning, office workflows, and deep research, with English and Chinese support and a context length of up to 262K tokens. This entry uses the Q8_0 GGUF quantization.

Repository: localaiLicense: apache-2.0

ced-base-q8
CED (Consistent Ensemble Distillation, Xiaomi) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). This is the q8_0 GGUF for the ced backend: smallest footprint (~88 MB, ~6.5x less memory than the PyTorch reference) and near-lossless (identical top-5 tags). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.

Repository: localaiLicense: apache-2.0

ced-tiny-q8
CED-tiny (5.5M params, Pi-class / edge) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). q8_0 GGUF for the ced backend (smallest footprint, near-lossless). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.

Repository: localaiLicense: apache-2.0

ced-mini-q8
CED-mini (9.6M params, low-power) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). q8_0 GGUF for the ced backend (smallest footprint, near-lossless). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.

Repository: localaiLicense: apache-2.0

ced-small-q8
CED-small (22M params, balanced size/accuracy) sound-event classifier over the 527-class AudioSet ontology (baby cry, footsteps, glass breaking, alarms, dog bark, ...). q8_0 GGUF for the ced backend (smallest footprint, near-lossless). Use POST /v1/audio/classification, or the realtime websocket API for live recognition.

Repository: localaiLicense: apache-2.0

omnilingual-0.3b-ctc-q8-sherpa
Omnilingual ASR CTC 300M (int8) is a multilingual automatic speech recognition model supporting 1,600+ languages. Based on Meta's omniASR_CTC_300M architecture (Wav2Vec2 with CTC head), quantized to int8 for efficient inference. Uses the sherpa-onnx backend with ONNX Runtime.

Repository: localaiLicense: apache-2.0

streaming-zipformer-en-sherpa
Streaming English ASR: sherpa-onnx zipformer transducer (int8, chunk-16 left-128). Low-latency real-time transcription with endpoint detection via sherpa-onnx's online recognizer. English-only; for multilingual offline ASR see omnilingual-0.3b-ctc-q8-sherpa.

Repository: localaiLicense: apache-2.0

moss-tts-cpp-v1_5-q8_0
MOSS-TTS-Local v1.5 (C++/ggml, moss-tts.cpp), Q8_0 (~6 GB), near-lossless. Native C++ text-to-speech for the OpenMOSS MOSS-TTS-Local Transformer v1.5 (a GPT-J local transformer decoded through the MOSS-Audio-Tokenizer-v2 neural codec), 48kHz stereo output with reference-audio voice cloning (set `voice` to a reference .wav). Verified per component against the reference PyTorch and about 2x faster per frame on CPU.

Repository: localaiLicense: apache-2.0

magpie-tts-cpp-357m-q8_0
Magpie TTS Multilingual 357M (C++/ggml, magpie-tts.cpp), Q8_0 (~624 MB), near-lossless and the fastest decode. Native C++ text-to-speech for NVIDIA's Magpie TTS Multilingual (encoder + autoregressive decoder over NanoCodec tokens) from one self-contained GGUF (model, codec, tokenizer, G2P dictionaries). 22.05kHz mono, 5 baked voices (set `voice` to Aria, Jason, John, Leo, Sofia or 0-4), 9+ languages (en, es, de, fr, it, pt-BR, hi, vi, ko, plus Arabic variants). Parity-gated per component against the NeMo reference.

Repository: localaiLicense: other

opengvlab_internvl3_5-30b-a3b-q8_0
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-14b-q8_0
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-8b-q8_0
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-4b-q8_0
We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 ×\times× inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Repository: localaiLicense: apache-2.0

depth-anything-3-base-q8_0
Depth Anything 3 (base), q8_0 — near-lossless 8-bit quant (~149 MB). Same depth + camera pose output as the q4_k default at higher fidelity.

Repository: localaiLicense: apache-2.0

depth-anything-2-base-q8_0
Depth Anything V2 (base / ViT-B), q8_0 — near-lossless 8-bit quant. Same relative monocular depth output as the q4_k default at higher fidelity. Use GenerateImage (src -> depth PNG) or the Depth endpoint.

Repository: localaiLicense: apache-2.0

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