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View details ↗Gemory-26B-A4B-GGUF
AbirayGemory-26B-A4B is a 26-billion parameter Mixture of Experts (MoE) model built on the Gemma architecture foundation, fine-tuned for high-fidelity creative writing, unrestricted roleplay, and complex multi-turn conversational depth. All quantizations were generated directly from the uncompressed 50.5 GB `BF16` base model and sanity-tested for generation integrity. **Command-Line Inference (`llama-cli`):** ```bash ./llama-cli \ -hf Abiray/Gemory-26B-A4B-GGUF:Q4_K_M \ -p "Write an opening scene set in a rain-soaked neon alleyway." \ -n 512 \ --temp 0.7
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View details ↗harrier-oss-v1-27b-GGUF
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View details ↗This repository contains static GGUF quantizations of **Hemmingway-1-heretic**, created by applying the arelath/Hemmingway-1-heretic-adapter to Altworld/Hemmingway-1 (a 27B model built on Qwen3.8-27B) and quantizing for local inference with `llama.cpp`. Hemmingway-1 is tuned specifically to write like a real person—skipping preambles, lecture notes, and corporate filler. This **heretic** version decensors the weights via targeted representation abliteration using Heretic v2.0.0, reducing refusals to near-zero while preserving high-EQ conversational voice. **License:** Governed by the original model's license terms: CC BY-NC 4.0 (Free for personal and non-commercial research use; commercial usage requires agreement with Altworld).
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View details ↗We provide a spectrum of quantizations to suit different hardware capabilities. If you are unsure which to choose, **Q4_K_M** is the recommended standard for the best balance of speed, size, and reasoning retention. These GGUF files are fully compatible with popular local AI software, including: * **LM Studio** * **Ollama** * **Jan** * **text-generation-webui (Oobabooga)** * **llama.cpp** Qwen models strictly use the ChatML format for prompting. Your system prompt and user inputs must be wrapped exactly like this:
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View details ↗This repository provides GGUF format quantizations for the huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated model. Because this model has been fully "abliterated" to bypass alignment and safety refusals, it acts as a highly capable engine for unrestricted creative writing, dynamic storytelling, and immersive roleplay scenarios. These models are designed to be run directly via `llama.cpp`. The following commands are standard for local Linux environments (such as Linux Mint or Ubuntu).
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View details ↗Krea-2-Turbo-GGUF
AbirayThis repository contains GGUF format quantizations of **Krea 2 Turbo**, a state-of-the-art 12-billion parameter Diffusion Transformer (DiT) text-to-image model developed by Krea.ai. GGUF weights allow for efficient, low-memory inference across consumer hardware, CPUs, and Apple Silicon. You can run these GGUF text-to-image files directly using compatible implementations such as `stable-diffusion.cpp`.
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View details ↗Lance_3B_Video-GGUF
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View details ↗The base model, LiquidAI/LFM2.5-2.6B, is a highly efficient 2.69 billion parameter model built specifically for on-device agentic workflows, multi-step instruction following, and tool calling. This specific iteration has been **abliterated (uncensored)** to remove safety refusals and guardrails, allowing the model to act as a fully compliant, unrestricted local agent while preserving the core intelligence, tool-calling capabilities, and the massive 128K context window of the original model. The abliteration process targets the refusal directions within the model's residual stream. By neutralizing these vectors, the model's tendency to reject controversial, explicit, or hypothetical prompts is heavily suppressed without lobotomizing its reasoning capabilities.
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View details ↗This repository contains the **LTX-Video 2.3 (v1.1)** Distilled weights, quantized to **GGUF** format. GGUF offers highly efficient inference and excellent memory scaling, allowing you to run this heavy 22B parameter model on consumer hardware by splitting the workload between your GPU (VRAM) and CPU (System RAM). To use these GGUF files in **ComfyUI**, you will need a custom node suite that supports loading GGUF models (such as `ComfyUI-GGUF`).
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View details ↗LTX-2.5-Distilled-GGUF
AbirayThis repository provides quantized GGUF formats of the distilled transformer from Lightricks/LTX-2.5. These weights are highly optimized for local execution, allowing you to run high-fidelity video and audio generation workflows on hardware with memory constraints while retaining the core visual fidelity of the original base model. LTX-2.5 operates on a split-component architecture. To run these GGUF diffusion models in environments like ComfyUI or local Python pipelines, you must also fetch the official Text Encoders and VAEs directly from the upstream Lightricks/LTX-2.5 repository. Download these from the `text_encoders` directory:
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View details ↗LTX2.3-10Eros-1.3-GGUF
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View details ↗LTX2.3-10Eros-GGUF
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View details ↗This repository provides quantized **GGUF** checkpoints for **MiniCPM-V 4.7 1B**, based on the refusal-abliterated weights created via **Heretic v1.4.0**. MiniCPM-V 4.7 1B is an ultra-efficient on-device multimodal large language model (MLLM) developed by OpenBMB. This version has been neutralized against standard refusal directions while retaining full multimodal vision and reasoning capabilities. The ablation was performed using **Heretic v1.4.0** to eliminate refusal bias while keeping Kullback–Leibler (KL) divergence near reference baseline:
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View details ↗MiniCPM5-1B-GGUF
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