This repository provides **two distinct formats** depending on your runtime: 1. **🔥 NEW: Unified Single-File Hybrid GGUFs (`*_vision_q8_0.gguf`)** — Combines the K-quantized LLM backbone (`Q4_K_M` / `Q5_K_M`) and the `Q8_0` Vision Tower + DeepStack Mergers + 5D `Conv3d` shape metadata (`comfy.gguf.orig_shape`) inside a **single file**. 2. **Split LLM + Standalone `mmproj` GGUFs** — Standard two-file format for **llama.cpp**, **llama-server**, **Ollama**, and split VLM loaders. *Contains both the Language Backbone (`blk.*`) and Vision Tower (`model.visual.*`) in a single GGUF file.* 1. Download either `qwen3vl_8b_q4_k_m_vision_q8_0.gguf` or `qwen3vl_8b_q5_k_m_vision_q8_0.gguf` and place it into:
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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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This repository contains GGUF format quantizations of **OvisOCR2**, a compact 0.8B end-to-end model for page-level document parsing. The original model was developed by **ATH-MaaS** by post-training `Qwen3.5-0.8B` to parse full document pages directly into clean Markdown (including LaTeX formulas, HTML tables, and layout components). OvisOCR2 establishes a new state-of-the-art for compact document understanding, scoring **96.58** on OmniDocBench v1.6 and outperforming traditional, multi-stage layout analysis pipelines. *Note: Because OvisOCR2 is a vision-language model, you **must** download one of these image processing units alongside your choice of the text models listed above.*
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These files were quantized by Abiray using `llama.cpp` to make the model accessible for consumer hardware and CPU-heavy environments. I have processed this model into several different quantization formats. You can find them in my other repositories: * Abiray/Qwen3.6-35B-A3B-Q8_0-GGUF (High quality, largest file) * Abiray/Qwen3.6-35B-A3B-Q6_K-GGUF (Very high quality) * Abiray/Qwen3.6-35B-A3B-Q5_K_M-GGUF (Great balance of quality and size) * Abiray/Qwen3.6-35B-A3B-Q4_K_M-GGUF (Good quality, recommended for most users) * Abiray/Qwen3.6-35B-A3B-Q3_K_M-GGUF (Low quality, smallest file)
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This repository contains the GGUF format quantization of the Qwen3.8-27B model, a native vision-language model. Qwen3.8-27B is the most capable generation in the Qwen open-model family, built on the architectural foundation of Qwen3.5. It is a native vision-language model that understands images and videos, delivering substantial gains across coding, professional work, research, and long-horizon agentic tasks. This GGUF file is compatible with popular local LLM inference tools. When configuring the model in your preferred client, ensure you attach the mmproj-F16.gguf file in the "Vision Adapter" or "Multimedia Projection" setting to enable image processing. The original model was developed and released by the Qwen Team.
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A standalone, **weight-baked uncensored & abliterated GGUF** of **`ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF`.** This build is created by precisely transplanting 144 orthogonalized residual-writing tensors from **`orcarouter/Qwen3.8-Flash-Next-Uncensored`** (via `SC117/Qwen3.8-Flash-Next-GSQ-RCO-abliterated-GGUF`) directly into the optimized 256-expert pruned Coder architecture. `ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF` compresses `Qwen3.8-Flash-Next` from 354 GB (BF16, 512 experts per layer) down to **58.4 GB** by combining: 1. **Structured Expert Pruning ($512 \rightarrow 256$ experts per layer):** Selects the 256 experts specialized for software engineering, coding, agentic tool calling, and vision. 2. **GSQ (Gumbel-Softmax Quantization) + RCO (Riemannian Co-design Search):** Assigns optimal mixed-precision GGUF quantization types across individual tensors while storing the retained weights at `~3.5 bits` (`IQ3_S`-equivalent fidelity, labeled `IQ1_M` for its `1.89` effective bits per parameter relative to the unpruned 512-expert model). --- Because this is an ultra-low-bit MoE model (`IQ1_M` effective / `IQ3_S` retained weights), **avoid pure greedy decoding (`--temp 0.0` with no repetition penalty)** on repetitive C/C++ struct definitions. Use standard Qwen sampling parameters with a light repetition/presence penalty:
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Abliterant GGUF quantizations of **abliterant/Qwen3.8-27B-RANA-abliterated**, a refusal-ablated **Qwen/Qwen3.8-27B**, for llama.cpp and compatible apps. Vision (`mmproj-*`) and the MTP speculative-decoding head (`mtp-*`) are included as separate files, in the same layout as ggml-org/Qwen3.8-27B-GGUF. Try RANA in Abliterant Chat · abliterant.ai · Research notes · All formats > **This is a safety-alignment-removed research model.** Read Intended use and > Limitations before using it. Method, full evaluation and release gates are on the > BF16 card.
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Occamy-1.0 · APEX GGUF Three plain profiles · four calibrated profiles, including Mini Collection · Checkpoint explorer · Project · Paper Occamy-1.0 quantized with the LocalAI team's APEX profiles. APEX allocates GGUF precision by tensor role and layer position: routed experts, shared experts and attention receive different allocations. The three plain profiles and four calibrated `I-` profiles use the pinned upstream 40-layer configurations. Every file is generated directly from the same BF16 source. The calibrated group adds I-Compact, I-Quality, I-Balanced and I-Mini. Profile names describe allocation strategies; file size does not follow the name order. These are weight-file sizes, not peak memory requirements. Allow additional memory for the runtime, context and image input.
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