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granite-4.2-30b-q4
IBM Granite 4.2 30B is the family's flagship multilingual reasoning model
for chat, coding, long-context tasks, and tool use. This entry uses the
Q4_K_M GGUF; a higher-fidelity Q8_0 build is available as a variant.
Repository: localaiLicense: apache-2.0
granite-4.2-30b-q8
IBM Granite 4.2 30B in the higher-fidelity Q8_0 GGUF format. It is the
family's flagship multilingual reasoning model for chat, coding, and tool
use.
Repository: localaiLicense: apache-2.0
nemotron-3.5-lightning-30b-a3b-q4
NVIDIA Nemotron 3.5 Lightning is a text-only hybrid Mamba-2, attention, and
mixture-of-experts model with 30B total parameters and 3B active parameters.
It targets reasoning, coding, tool use, multilingual chat, and long-context
agent workflows, with a context window of up to one million tokens.
This entry uses the official Q4_K_M GGUF. Automatic variant selection can
choose the smaller NVFP4 build or the higher-quality Q8_0 build when it fits.
Repository: localaiLicense: openmdw-1.1
nemotron-3.5-lightning-30b-a3b-nvfp4
NVIDIA Nemotron 3.5 Lightning 30B-A3B in the official NVFP4 GGUF format.
This is the smallest linked build and retains the model's reasoning, coding,
tool-use, multilingual, and long-context capabilities.
Repository: localaiLicense: openmdw-1.1
nemotron-3.5-lightning-30b-a3b-q8
NVIDIA Nemotron 3.5 Lightning 30B-A3B in the official high-quality Q8_0
GGUF format for hosts with enough memory.
Repository: localaiLicense: openmdw-1.1
muse-glimmer-30b
Muse Glimmer is Meta Superintelligence Labs' Apache-2.0 dense 30B model for
autonomous agentic work, coding, tool use, long-horizon reasoning, and
multimodal understanding. It supports more than 100 languages, interleaved
text and image input through its 1.8B-parameter perception encoder, and a
131K-token context window.
This entry uses the publisher's higher-quality dynamic K-quant GGUF and
official quantized vision projector. Automatic variant selection can use
the smaller 17 GB quantization or a DFlash-accelerated build when it fits.
Repository: localaiLicense: apache-2.0
muse-glimmer-30b-dflash
Muse Glimmer's higher-quality dynamic K-quant GGUF with the official
quantized perception encoder and DFlash drafter. DFlash proposes blocks of
up to 16 tokens for the target to verify in parallel, accelerating output
without changing model quality. Flash attention is enabled for this path.
Repository: localaiLicense: apache-2.0
muse-glimmer-30b-17gb
Muse Glimmer's smaller 17 GB K-quant GGUF with the official quantized
perception encoder. It preserves the model's agentic, coding, tool-use,
multilingual, and image-understanding capabilities for hosts with less
memory than the dynamic quantization requires.
Repository: localaiLicense: apache-2.0
muse-glimmer-30b-17gb-dflash
Muse Glimmer's smaller 17 GB K-quant GGUF with the official quantized
perception encoder and DFlash drafter. This is the lowest-memory published
build that retains image understanding and block-speculative decoding.
Flash attention is enabled for the DFlash path.
Repository: localaiLicense: apache-2.0
homura-30b-q4
Homura 30B is an English, agent-focused fine-tune of Muse Glimmer 30B.
It targets autonomous tool use and direct instruction following. This
entry uses the publisher's 16.9 GB Q4_K_M GGUF and supports a 131K-token
context window.
Repository: localaiLicense: apache-2.0
nemotron-3-nano-omni-30b-a3b-reasoning-apex
# Model Overview
### Description:
NVIDIA Nemotron 3 Nano Omni is a multimodal large language model that unifies video, audio, image, and text understanding to support enterprise-grade Q&A, summarization, transcription, and document intelligence workflows. It extends the Nemotron Nano family with integrated video+speech comprehension, Graphical User Interface (GUI), Optical Character Recognition (OCR), and speech transcription capabilities, enabling end-to-end processing of rich enterprise content such as meeting recordings, M&E assets, training videos, and complex business documents. NVIDIA Nemotron 3 Nano Omni was developed by NVIDIA as part of the Nemotron model family.
This model is available for commercial use.
This model was improved using Qwen3-VL-30B-A3B-Instruct, Qwen3.5-122B-A10B, Qwen3.5-397B-A17B, Qwen2.5-VL-72B-Instruct, and gpt-oss-120b. For more information, please see the Training Dataset section below.
### License/Terms of Use
Governing Terms: Use of this model is governed by the NVIDIA Open Model Agreement
### Deployment Geography:
Global
...
Repository: localaiLicense: other
qwen3-coder-30b-a3b-vllm-cpp
Qwen3-Coder-30B-A3B on vllm.cpp: a coding and agentic-tool-use model, 30B
total parameters with about 3B active per token, gated token-exact against
vLLM on this engine.
The tool-call parser is named explicitly rather than auto-detected, and that
matters here. Qwen3-Coder's tool dialect is byte-identical on the wire to
another family's, so template sniffing cannot separate the two and would fall
back to the wrong parser. With qwen3_coder named, tool calls arrive as real
tool_calls on the OpenAI response.
This is the bf16 checkpoint, roughly 57 GB of weights, which is what the
engine was gated on. Being bf16 rather than NVFP4 it does not need Blackwell
on its own account, but LocalAI's CUDA images for this backend are currently
built for Blackwell-family GPUs only, so on an older card use the CPU build.
Repository: localaiLicense: apache-2.0
vllm-omni-qwen3-omni-30b
Qwen3-Omni-30B-A3B-Instruct via vLLM-Omni - A large multimodal model (30B active, 3B activated per token) from Alibaba Qwen team. Supports text, image, audio, and video understanding with text and speech output. Features native multimodal understanding across all modalities.
Repository: localaiLicense: apache-2.0
glm-4.7-flash
**GLM-4.7-Flash** is a 30B-A3B MoE (Model Organism Ensemble) model designed for efficient deployment. It outperforms competitors in benchmarks like AIME 25, GPQA, and τ²-Bench, offering strong accuracy while balancing performance and efficiency. Optimized for lightweight use cases, it supports inference via frameworks like vLLM and SGLang, with detailed deployment instructions in the official repository. Ideal for applications requiring high-quality text generation with minimal resource consumption.
Repository: localaiLicense: mit
tildeopen-30b-instruct-lv-i1
The **TildeOpen-30B-Instruct-LV-i1-GGUF** is a quantized version of the base model **pazars/TildeOpen-30B-Instruct-LV**, optimized for deployment. It is an instruct-based language model trained on diverse datasets, supporting multiple languages (en, de, fr, pl, ru, it, pt, cs, nl, es, fi, tr, hu, bg, uk, bs, hr, da, et, lt, ro, sk, sl, sv, no, lv, sr, sq, mk, is, mt, ga). Licensed under CC-BY-4.0, it uses the Transformers library and is designed for efficient inference. The quantized version (with imatrix format) is tailored for deployment on devices with limited resources, while the base model remains the original, high-quality version.
Repository: localaiLicense: cc-by-4.0
qwen3-coder-30b-a3b-instruct-rtpurbo-i1
The model in question is a quantized version of the original **Qwen3-Coder** large language model, specifically tailored for code generation. The base model, **RTP-LLM/Qwen3-Coder-30B-A3B-Instruct-RTPurbo**, is a 30B-parameter variant optimized for instruction-following and code-related tasks. It employs the **A3B attention mechanism** and is trained on diverse data to excel in programming and logical reasoning. The current repository provides a quantized (compressed) version of this model, which is suitable for deployment on hardware with limited memory but loses some precision compared to the original. For a high-fidelity version, the unquantized base model is recommended.
Repository: localai
qwen3-vl-30b-a3b-instruct
Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.
This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.
Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on-demand deployment.
#### Key Enhancements:
* **Visual Agent**: Operates PC/mobile GUIs—recognizes elements, understands functions, invokes tools, completes tasks.
* **Visual Coding Boost**: Generates Draw.io/HTML/CSS/JS from images/videos.
* **Advanced Spatial Perception**: Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for spatial reasoning and embodied AI.
* **Long Context & Video Understanding**: Native 256K context, expandable to 1M; handles books and hours-long video with full recall and second-level indexing.
* **Enhanced Multimodal Reasoning**: Excels in STEM/Math—causal analysis and logical, evidence-based answers.
* **Upgraded Visual Recognition**: Broader, higher-quality pretraining is able to “recognize everything”—celebrities, anime, products, landmarks, flora/fauna, etc.
* **Expanded OCR**: Supports 32 languages (up from 19); robust in low light, blur, and tilt; better with rare/ancient characters and jargon; improved long-document structure parsing.
* **Text Understanding on par with pure LLMs**: Seamless text–vision fusion for lossless, unified comprehension.
#### Model Architecture Updates:
1. **Interleaved-MRoPE**: Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning.
2. **DeepStack**: Fuses multi‑level ViT features to capture fine-grained details and sharpen image–text alignment.
3. **Text–Timestamp Alignment:** Moves beyond T‑RoPE to precise, timestamp‑grounded event localization for stronger video temporal modeling.
This is the weight repository for Qwen3-VL-30B-A3B-Instruct.
Repository: localaiLicense: apache-2.0
qwen3-vl-30b-a3b-thinking
Qwen3-VL-30B-A3B-Thinking is a 30B parameter model that is thinking.
Repository: localaiLicense: apache-2.0
huihui-qwen3-vl-30b-a3b-instruct-abliterated
These are quantizations of the model Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated-GGUF
Repository: localaiLicense: apache-2.0
qwen3-omni-30b-a3b-instruct
Qwen3-Omni is the natively end-to-end multilingual omni-modal foundation model. It processes text, images, audio, and video, and delivers real-time streaming responses in both text and natural speech. This GGUF build runs on llama.cpp with the bundled mmproj for multimodal inputs.
Repository: localaiLicense: apache-2.0
qwen3-omni-30b-a3b-thinking
Qwen3-Omni-30B-A3B-Thinking is the reasoning-enhanced variant of Qwen3-Omni, a natively end-to-end multilingual omni-modal foundation model. It processes text, images, and audio and produces chain-of-thought reasoning before the final answer. This GGUF build runs on llama.cpp with the bundled mmproj.
Repository: localaiLicense: apache-2.0
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