This repository disseminates a comprehensive suite of 27 GGUF (GPT-Generated Unified Format) quantizations of **Muse-Glimmer-30B-Heretic-Uncensored**, an abliterated derivative of Meta's Muse-Glimmer-30B produced with Heretic v1.4.0 by darkc0de. The release spans a memory footprint of 6.53 GB (IQ1_S) to 55.73 GB (F16/BF16), thereby accommodating deployment across heterogeneous hardware, from CPU-only systems to 32 GB VRAM accelerators. All sub-4-bit quantizations were calibrated with an importance matrix (imatrix) to mitigate quality degradation. The fidelity of the Q4_K_S variant relative to the F16 reference is quantified via token-level perplexity on a fixed evaluation set. The model exhibits **zero refusals on adult/NSFW content**; this property was empirically verified. The quantization selection is derived from the model's architectural properties and the memory constraints of representative target hardware. The model employs **grouped-query attention with 2 KV heads**, which renders the KV cache exceptionally compact: All artifacts were derived from the original safetensors using llama.cpp **master** (commit `030ebb5`). Support for the muse_glimmer architecture is absent from release binaries prior to b10344; consequently, a master-branch build was required.
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GSQ-RCO upstream · layout preserved 144 tensors transplanted IQ3_S / IQ3_XXS / IQ2_XS / Q2_0 per-tensor blake2b verified 262K context APACHE-2.0 Qwen3.8-Flash-Next · GSQ-RCO-abliterated The refusal direction removed from an already-quantized GSQ-RCO model — 144 tensors across all 48 layers swapped for ready-made abliterated weights, with every value and scale GSQ learned left untouched and the upstream per-tensor type assignment restored exactly . The IQ3_S build is verified tensor by tensor: everything that changed was supposed to, nothing else moved. 🧭 What this repository is An abliterated build of the official ISTA-DASLab GSQ-RCO quantizations . Not a single one of GSQ's learned quantized tensors was recomputed — 144 "write-to-residual-stream" projection tensors across all 48 layers were replaced with the corresponding tensors from a ready-made abliterated release. Base ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF — GSQ learned quantization + RCO budget-constrained type assignment Ablation source orcarouter/Qwen3.8-Flash-Next-Uncensored-GGUF — the 144 target tensors are taken straight from it Method Byte-level GGUF → GGUF tensor transplant (135 of 144 targets); the 9 remaining expert tensors, which upstream stores as Q2_0, are re-encoded from the abliterated values using GSQ's own learned block scales — no GSQ value is recomputed, only the codes Not done Any dequantize → quantize cycle; any re-quantization from BF16 / safetensors In one line: what you get is still the GSQ-RCO quantization you already know , with those 144 tensors abliterated. It is the only abliteration route that preserves GSQ's work. ⚠️ Safety notice This model is abliterated and refusal-removed — it has no meaningful built-in guardrails and may comply with harmful, illegal or unsafe requests. Deploy it only where you can supply your own moderation, access control and legal review; do not put it in front of end users without a safety layer of your own. 本模型经过拒答方向消融，不具备可靠的内置安全护栏，可能配合有害、违法或不安全的请求。请仅在你能够提供审核、访问控制与法律审查的场景部署。
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A 27B dense vision-language model (27.8B including the vision tower). 64 decoder layers in a hybrid stack: `full_attention_interval=4` means every fourth layer is grouped-query full attention (16 layers) and the rest are Gated DeltaNet linear attention (48 layers). Vocab 248320 with untied embeddings. Context length 262144 natively, extensible further with YaRN. The mmproj file is separate by design: llama.cpp loads the text backbone and the vision projector as two files. Download one tier plus one mmproj. Without the mmproj the model still runs, but text-only. The `mmproj-` and `mtp-` prefixes are what recent llama.cpp builds match on to tell the three file kinds apart, so `-hf 6block/Qwen3.8-27B-GGUF` picks a backbone rather than a projector, and `--spec-type mtp` finds the draft on its own.
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This repository contains W4A16 quantized versions of Google's Gemma 3 instruction-tuned models, making them more accessible for deployment on consumer hardware while maintaining good performance. - **abhishekchohan/gemma-3-27b-it-quantized-W4A16** - **abhishekchohan/gemma-3-12b-it-quantized-W4A16** - **abhishekchohan/gemma-3-4b-it-quantized-W4A16** These models use W4A16 quantization via LLM Compressor: - Weights quantized to 4-bit precision - Activations use 16-bit precision - Significantly reduced memory requirements
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abhishekchohan/Qwen3.8-27B-AWQ-INT4 with a calibrated **fp8-e4m3 KV cache**. The weights are byte-identical to the source AWQ model; this checkpoint adds measured K/V quantization scales so the model serves its full 262,144-token context with half the KV memory of bf16. The scales were measured on the quantized model itself, not on the BF16 base, because activation-aware smoothing and int4 rounding change the K/V activations the served model actually writes to cache. Calibration replayed the exact same sequences that were used to calibrate the AWQ model: **112 packed sequences × 262,144 tokens (~29.4M tokens)** from the same Nemotron Post-Training v3 SFT blend (instruction-following chat, math, science, agentic code, multilingual), same shuffle seed, rendered with thinking enabled, so the measured KV distribution matches the deployment regime the weights were tuned for, at the exact context length the model serves at.
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