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qwen_qwen3.5-35b-a3b

Repository: localai

qwen_qwen3-next-80b-a3b-thinking

Repository: localai

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

baidu_ernie-4.5-21b-a3b-thinking
Over the past three months, we have continued to scale the thinking capability of ERNIE-4.5-21B-A3B, improving both the quality and depth of reasoning, thereby advancing the competitiveness of ERNIE lightweight models in complex reasoning tasks. We are pleased to introduce ERNIE-4.5-21B-A3B-Thinking, featuring the following key enhancements: Significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, text generation, and academic benchmarks that typically require human expertise. Efficient tool usage capabilities. Enhanced 128K long-context understanding capabilities. Note: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks. ERNIE-4.5-21B-A3B-Thinking is a text MoE post-trained model, with 21B total parameters and 3B activated parameters for each token.

Repository: localaiLicense: apache-2.0

opengvlab_internvl3_5-30b-a3b
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_5-30b-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

qwen3-30b-a3b
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation. Qwen3-30B-A3B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 30.5B in total and 3.3B activated Number of Paramaters (Non-Embedding): 29.9B Number of Layers: 48 Number of Attention Heads (GQA): 32 for Q and 4 for KV Number of Experts: 128 Number of Activated Experts: 8 Context Length: 32,768 natively and 131,072 tokens with YaRN. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Repository: localaiLicense: apache-2.0

qwen3-30b-a3b-abliterated
Abliterated version of Qwen3-30B-A3B by mlabonne.

Repository: localaiLicense: apache-2.0

kalomaze_qwen3-16b-a3b
A man-made horror beyond your comprehension. But no, seriously, this is my experiment to: measure the probability that any given expert will activate (over my personal set of fairly diverse calibration data), per layer prune 64/128 of the least used experts per layer (with reordered router and indexing per layer) It can still write semi-coherently without any additional training or distillation done on top of it from the original 30b MoE. The .txt files with the original measurements are provided in the repo along with the exported weights. Custom testing to measure the experts was done on a hacked version of vllm, and then I made a bespoke script to selectively export the weights according to the measurements.

Repository: localaiLicense: apache-2.0

gryphe_pantheon-proto-rp-1.8-30b-a3b
Note: This model is a Qwen 30B MoE prototype and can be considered a sidegrade from my Small release some time ago. It did not receive extensive testing beyond a couple benchmarks to determine its sanity, so feel free to let me know what you think of it! Welcome to the next iteration of my Pantheon model series, in which I strive to introduce a whole collection of diverse personas that can be summoned with a simple activation phrase. Pantheon's purpose is two-fold, as these personalities similarly enhance the general roleplay experience, helping to encompass personality traits, accents and mannerisms that language models might otherwise find difficult to convey well. GGUF quants are available here. Your user feedback is critical to me so don't hesitate to tell me whether my model is either 1. terrible, 2. awesome or 3. somewhere in-between. Model details Ever since Qwen 3 released I've been trying to get MoE finetuning to work - After countless frustrating days, much code hacking, etc etc I finally got a full finetune to complete with reasonable loss values. I picked the base model for this since I didn't feel like trying to fight a reasoning model's training - Maybe someday I'll make a model which uses thinking tags for the character's thoughts or something. This time the recipe focused on combining as many data sources as I possibly could, featuring synthetic data from Sonnet 3.5 + 3.7, ChatGPT 4o and Deepseek. These then went through an extensive rewriting pipeline to eliminate common AI cliches, with the hopeful intent of providing you a fresh experience.

Repository: localaiLicense: apache-2.0

allura-org_q3-30b-a3b-pentiment
Triple stage RP/general tune of Qwen3-30B-A3b Base (finetune, merged for stablization, aligned)

Repository: localaiLicense: apache-2.0

allura-org_q3-30b-a3b-designant
Intended as a direct upgrade to Pentiment, Q3-30B-A3B-Designant is a roleplaying model finetuned from Qwen3-30B-A3B-Base. During testing, Designant punched well above its weight class in terms of active parameters, demonstrating the potential for well-made lightweight Mixture of Experts models in the roleplay scene. While one tester observed looping behavior, repetition in general was minimal.

Repository: localaiLicense: apache-2.0

qwen3-55b-a3b-total-recall-v1.3-i1
WARNING: MADNESS - UN HINGED and... NSFW. Vivid prose. INTENSE. Visceral Details. Violence. HORROR. GORE. Swearing. UNCENSORED... humor, romance, fun. This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly. This model is for all use cases, but excels in creative use cases specifically. This model is based on Qwen3-30B-A3B (MOE, 128 experts, 8 activated), with Brainstorm 40X (by DavidAU - details at bottom of this page. This is the refined version -V1.3- from this project (see this repo for all settings, details, system prompts, example generations etc etc): https://huggingface.co/DavidAU/Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF/ This version -1.3- is slightly smaller, with further refinements to the Brainstorm adapter. This will change generation and reasoning performance within the model.

Repository: localaiLicense: apache-2.0

qwen3-55b-a3b-total-recall-deep-40x
WARNING: MADNESS - UN HINGED and... NSFW. Vivid prose. INTENSE. Visceral Details. Violence. HORROR. GORE. Swearing. UNCENSORED... humor, romance, fun. Qwen3-55B-A3B-TOTAL-RECALL-Deep-40X-GGUF A highly experimental model ("tamer" versions below) based on Qwen3-30B-A3B (MOE, 128 experts, 8 activated), with Brainstorm 40X (by DavidAU - details at bottom of this page). These modifications blow the model (V1) out to 87 layers, 1046 tensors and 55B parameters. Note that some versions are smaller than this, with fewer layers/tensors and smaller parameter counts. The adapter extensively alters performance, reasoning and output generation. Exceptional changes in creative, prose and general performance. Regens of the same prompt - even with the same settings - will be very different. THREE example generations below - creative (generated with Q3_K_M, V1 model). ONE example generation (#4) - non creative (generated with Q3_K_M, V1 model). You can run this model on CPU and/or GPU due to unique model construction, size of experts and total activated experts at 3B parameters (8 experts), which translates into roughly almost 6B parameters in this version. Two quants uploaded for testing: Q3_K_M, Q4_K_M V3, V4 and V5 are also available in these two quants. V2 and V6 in Q3_k_m only; as are: V 1.3, 1.4, 1.5, 1.7 and V7 (newest) NOTE: V2 and up are from source model 2, V1 and 1.3,1.4,1.5,1.7 are from source model 1.

Repository: localaiLicense: apache-2.0

qwen3-42b-a3b-stranger-thoughts-deep20x-abliterated-uncensored-i1
WARNING: NSFW. Vivid prose. INTENSE. Visceral Details. Violence. HORROR. GORE. Swearing. UNCENSORED... humor, romance, fun. Qwen3-42B-A3B-Stranger-Thoughts-Deep20x-Abliterated-Uncensored This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly. ABOUT: Qwen's excellent "Qwen3-30B-A3B", abliterated by "huihui-ai" then combined Brainstorm 20x (tech notes at bottom of the page) in a MOE (128 experts) at 42B parameters (up from 30B). This pushes Qwen's abliterated/uncensored model to the absolute limit for creative use cases. Prose (all), reasoning, thinking ... all will be very different from reg "Qwen 3s". This model will generate horror, fiction, erotica, - you name it - in vivid, stark detail. It will NOT hold back. Likewise, regen(s) of the same prompt - even at the same settings - will create very different version(s) too. See FOUR examples below. Model retains full reasoning, and output generation of a Qwen3 MOE ; but has not been tested for "non-creative" use cases. Model is set with Qwen's default config: 40 k context 8 of 128 experts activated. Chatml OR Jinja Template (embedded) IMPORTANT: See usage guide / repo below to get the most out of this model, as settings are very specific. USAGE GUIDE: Please refer to this model card for Specific usage, suggested settings, changing ACTIVE EXPERTS, templates, settings and the like: How to maximize this model in "uncensored" form, with specific notes on "abliterated" models. Rep pen / temp settings specific to getting the model to perform strongly. https://huggingface.co/DavidAU/Qwen3-18B-A3B-Stranger-Thoughts-Abliterated-Uncensored-GGUF GGUF / QUANTS / SPECIAL SHOUTOUT: Special thanks to team Mradermacher for making the quants! https://huggingface.co/mradermacher/Qwen3-42B-A3B-Stranger-Thoughts-Deep20x-Abliterated-Uncensored-GGUF KNOWN ISSUES: Model may "mis-capitalize" word(s) - lowercase, where uppercase should be - from time to time. Model may add extra space from time to time before a word. Incorrect template and/or settings will result in a drop in performance / poor performance.

Repository: localaiLicense: apache-2.0

qwen3-22b-a3b-the-harley-quinn
WARNING: MADNESS - UN HINGED and... NSFW. Vivid prose. INTENSE. Visceral Details. Violence. HORROR. GORE. Swearing. UNCENSORED... humor, romance, fun. Qwen3-22B-A3B-The-Harley-Quinn This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly. ABOUT: A stranger, yet radically different version of Kalmaze's "Qwen/Qwen3-16B-A3B" with the experts pruned to 64 (from 128, the Qwen 3 30B-A3B version) and then I added 19 layers expanding (Brainstorm 20x by DavidAU info at bottom of this page) the model to 22B total parameters. The goal: slightly alter the model, to address some odd creative thinking and output choices. Then... Harley Quinn showed up, and then it was a party! A wild, out of control (sometimes) but never boring party. Please note that the modifications affect the entire model operation; roughly I adjusted the model to think a little "deeper" and "ponder" a bit - but this is a very rough description. That being said, reasoning and output generation will be altered regardless of your use case(s). These modifications pushes Qwen's model to the absolute limit for creative use cases. Detail, vividiness, and creativity all get a boost. Prose (all) will also be very different from "default" Qwen3. Likewise, regen(s) of the same prompt - even at the same settings - will create very different version(s) too. The Brainstrom 20x has also lightly de-censored the model under some conditions. However, this model can be prone to bouts of madness. It will not always behave, and it will sometimes go -wildly- off script. See 4 examples below. Model retains full reasoning, and output generation of a Qwen3 MOE ; but has not been tested for "non-creative" use cases. Model is set with Qwen's default config: 40 k context 8 of 64 experts activated. Chatml OR Jinja Template (embedded) Four example generations below. IMPORTANT: See usage guide / repo below to get the most out of this model, as settings are very specific. If not set correctly, this model will not work the way it should. Critical settings: Chatml or Jinja Template (embedded, but updated version at repo below) Rep pen of 1.01 or 1.02 ; higher (1.04, 1.05) will result in "Harley Mode". Temp range of .6 to 1.2. ; higher you may need to prompt the model to "output" after thinking. Experts set at 8-10 ; higher will result in "odder" output BUT it might be better. That being said, "Harley Quinn" may make her presence known at any moment. USAGE GUIDE: Please refer to this model card for Specific usage, suggested settings, changing ACTIVE EXPERTS, templates, settings and the like: How to maximize this model in "uncensored" form, with specific notes on "abliterated" models. Rep pen / temp settings specific to getting the model to perform strongly. https://huggingface.co/DavidAU/Qwen3-18B-A3B-Stranger-Thoughts-Abliterated-Uncensored-GGUF GGUF / QUANTS / SPECIAL SHOUTOUT: Special thanks to team Mradermacher for making the quants! https://huggingface.co/mradermacher/Qwen3-22B-A3B-The-Harley-Quinn-GGUF KNOWN ISSUES: Model may "mis-capitalize" word(s) - lowercase, where uppercase should be - from time to time. Model may add extra space from time to time before a word. Incorrect template and/or settings will result in a drop in performance / poor performance. Can rant at the end / repeat. Most of the time it will stop on its own. Looking for the Abliterated / Uncensored version? https://huggingface.co/DavidAU/Qwen3-23B-A3B-The-Harley-Quinn-PUDDIN-Abliterated-Uncensored In some cases this "abliterated/uncensored" version may work better than this version. EXAMPLES Standard system prompt, rep pen 1.01-1.02, topk 100, topp .95, minp .05, rep pen range 64. Tested in LMStudio, quant Q4KS, GPU (CPU output will differ slightly). As this is the mid range quant, expected better results from higher quants and/or with more experts activated to be better. NOTE: Some formatting lost on copy/paste. WARNING: NSFW. Vivid prose. INTENSE. Visceral Details. Violence. HORROR. GORE. Swearing. UNCENSORED... humor, romance, fun.

Repository: localaiLicense: apache-2.0

qwen3-33b-a3b-stranger-thoughts-abliterated-uncensored
WARNING: NSFW. Vivid prose. INTENSE. Visceral Details. Violence. HORROR. GORE. Swearing. UNCENSORED... humor, romance, fun. Qwen3-33B-A3B-Stranger-Thoughts-Abliterated-Uncensored This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly. ABOUT: A stranger, yet radically different version of "Qwen/Qwen3-30B-A3B", abliterated by "huihui-ai" , with 4 added layers expanding the model to 33B total parameters. The goal: slightly alter the model, to address some odd creative thinking and output choices AND de-censor it. Please note that the modifications affect the entire model operation; roughly I adjusted the model to think a little "deeper" and "ponder" a bit - but this is a very rough description. I also ran reasoning tests (non-creative) to ensure model was not damaged and roughly matched original model performance. That being said, reasoning and output generation will be altered regardless of your use case(s)

Repository: localaiLicense: apache-2.0

qwen_qwen3-30b-a3b-instruct-2507
We introduce the updated version of the Qwen3-30B-A3B non-thinking mode, named Qwen3-30B-A3B-Instruct-2507, featuring the following key enhancements: Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage. Substantial gains in long-tail knowledge coverage across multiple languages. Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. Enhanced capabilities in 256K long-context understanding.

Repository: localaiLicense: apache-2.0

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