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z-image-diffusers
Z-Image is the foundation model of the ⚡️-Image family, engineered for good quality, robust generative diversity, broad stylistic coverage, and precise prompt adherence. While Z-Image-Turbo is built for speed, Z-Image is a full-capacity, undistilled transformer designed to be the backbone for creators, researchers, and developers who require the highest level of creative freedom.

Repository: localaiLicense: apache-2.0

aurore-reveil_koto-small-7b-it
Koto-Small-7B-IT is an instruct-tuned version of Koto-Small-7B-PT, which was trained on MiMo-7B-Base for almost a billion tokens of creative-writing data. This model is meant for roleplaying and instruct usecases.

Repository: localaiLicense: mit

openai_gpt-oss-20b-neo
These are NEO Imatrix GGUFs, NEO dataset by DavidAU. NEO dataset improves overall performance, and is for all use cases. Example output below (creative), using settings below. Model also passed "hard" coding test too (6 experts); no issues (IQ4_NL). (Forcing the model to create code with no dependencies and limits of coding short cuts, with multiple loops, and in real time with no blocking in a language that does not support it normally.) Due to quanting issues with this model (which result in oddball quant sizes / mixtures), only TESTED quants will be uploaded (at the moment).

Repository: localaiLicense: apache-2.0

openai-gpt-oss-20b-abliterated-uncensored-neo-imatrix
These are NEO Imatrix GGUFs, NEO dataset by DavidAU. NEO dataset improves overall performance, and is for all use cases. This model uses Huihui-gpt-oss-20b-BF16-abliterated as a base which DE-CENSORS the model and removes refusals. Example output below (creative; IQ4_NL), using settings below. This model can be a little rough around the edges (due to abliteration) ; make sure you see the settings below for best operation. It can also be creative, off the shelf crazy and rational too. Enjoy!

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

qwen3-32b
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. Qwen3-32B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 32.8B Number of Paramaters (Non-Embedding): 31.2B Number of Layers: 64 Number of Attention Heads (GQA): 64 for Q and 8 for KV 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

qwen3-14b
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. Qwen3-14B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 14.8B Number of Paramaters (Non-Embedding): 13.2B Number of Layers: 40 Number of Attention Heads (GQA): 40 for Q and 8 for KV 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

qwen3-8b
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. Model Overview Qwen3-8B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 8.2B Number of Paramaters (Non-Embedding): 6.95B Number of Layers: 36 Number of Attention Heads (GQA): 32 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN.

Repository: localaiLicense: apache-2.0

qwen3-4b
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. Qwen3-4B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 4.0B Number of Paramaters (Non-Embedding): 3.6B Number of Layers: 36 Number of Attention Heads (GQA): 32 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN.

Repository: localaiLicense: apache-2.0

qwen3-1.7b
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. Qwen3-1.7B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 1.7B Number of Paramaters (Non-Embedding): 1.4B Number of Layers: 28 Number of Attention Heads (GQA): 16 for Q and 8 for KV Context Length: 32,768

Repository: localaiLicense: apache-2.0

qwen3-0.6b
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. Qwen3-0.6B has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 0.6B Number of Paramaters (Non-Embedding): 0.44B Number of Layers: 28 Number of Attention Heads (GQA): 16 for Q and 8 for KV Context Length: 32,768

Repository: localaiLicense: apache-2.0

shuttleai_shuttle-3.5
A fine-tuned version of Qwen3 32b, emulating the writing style of Claude 3 models and thoroughly trained on role-playing data. 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. Shuttle 3.5 has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 32.8B Number of Paramaters (Non-Embedding): 31.2B Number of Layers: 64 Number of Attention Heads (GQA): 64 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN.

Repository: localaiLicense: apache-2.0

qwen3-4b-esper3-i1
Esper 3 is a coding, architecture, and DevOps reasoning specialist built on Qwen 3. Finetuned on our DevOps and architecture reasoning and code reasoning data generated with Deepseek R1! Improved general and creative reasoning to supplement problem-solving and general chat performance. Small model sizes allow running on local desktop and mobile, plus super-fast server inference!

Repository: localaiLicense: apache-2.0

qwen3-the-josiefied-omega-directive-22b-uncensored-abliterated-i1
WARNING: NSFW. Vivid prose. INTENSE. Visceral Details. Violence. HORROR. GORE. Swearing. UNCENSORED... humor, romance, fun. A massive 22B, 62 layer merge of the fantastic "The-Omega-Directive-Qwen3-14B-v1.1" and off the scale "Goekdeniz-Guelmez/Josiefied-Qwen3-14B-abliterated-v3" in Qwen3, with full reasoning (can be turned on or off) and the model is completely uncensored/abliterated too.

Repository: localaiLicense: apache-2.0

qwen3-the-xiaolong-omega-directive-22b-uncensored-abliterated-i1
WARNING: NSFW. Vivid prose. INTENSE. Visceral Details. Violence. HORROR. GORE. Swearing. UNCENSORED... humor, romance, fun. A massive 22B, 62 layer merge of the fantastic "The-Omega-Directive-Qwen3-14B-v1.1" (by ReadyArt) and off the scale "Xiaolong-Qwen3-14B" (by nbeerbower) in Qwen3, with full reasoning (can be turned on or off) and the model is completely uncensored/abliterated too.

Repository: localaiLicense: apache-2.0

ds-r1-qwen3-8b-arliai-rpr-v4-small-iq-imatrix
The best RP/creative model series from ArliAI yet again. This time made based on DS-R1-0528-Qwen3-8B-Fast for a smaller memory footprint. Reduced repetitions and impersonation To add to the creativity and out of the box thinking of RpR v3, a more advanced filtering method was used in order to remove examples where the LLM repeated similar phrases or talked for the user. Any repetition or impersonation cases that happens will be due to how the base QwQ model was trained, and not because of the RpR dataset. Increased training sequence length The training sequence length was increased to 16K in order to help awareness and memory even on longer chats.

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

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