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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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**RVN** is a double-refined abliterated variant of **Qwen3.8-27B**, built on top of `trohrbaugh/Qwen3.8-27B-heretic-ara` (an ARA abliteration by **Tim Rohrbaugh**) and further refined with **two additional full-weight ARA passes** targeting residual refusals. It retains very low behavioral damage (KL ≈ 0.0085) while reducing harmful-prompt refusals from 3/100 (source) to **0–1/100** in independent measurements. > **Note on this repository's history.** This repo previously hosted the original > `Qwen3.8-27B-Heretic-Q4_K_M.gguf` (single-quant release from the earlier > `trohrbaugh/Qwen3.8-27B-heretic` source). That file is kept as **legacy** for > download-count continuity and backward compatibility — it is the *older* abliteration > variant and is superseded by the RVN files below. Prefer the RVN quants for new > deployments. > **Not for all audiences.** This model has reduced safety guardrails by design. It is > intended for adult audiences (18+) doing research, creative writing, roleplay, and > uncensored generation. Certain guardrails are intentionally left in place; use > responsibly and in accordance with your local laws.

**Non-uniform GGUF quantizations** of **RVN Qwen3.8-27B-Heretic-Abliterated-Uncensored** (refusals 0-1/100, KL ~ 0.0085 vs base). Instead of one uniform quantization type for all tensors, **each of the 851 tensors carries its own type**, following the per-tensor **RCO allocation** published by IST-DASLab for their GSQ-RCO release of Qwen3.8-27B, quantized locally with **llama.cpp** and the **official importance matrix** `imatrix-qwen3.8-27b.gguf`. Files are standard GGUF and run unmodified in llama.cpp / Ollama / LM Studio. > Regarding IQ3_S there is new method: GSQ-RCO, if it's possible to combine your abliteration method with this quantization, we would have a true refusal free Qwen3.8-27B running fully on 16GB cards. At **3.50 bpw the allocation lands within 1% of F16** - and a same-class uniform quantization sits 1.9% behind it **at a larger file size** (12.42 GB vs 11.80 GB). Generation speeds (same box, full offload): IQ2_XS 105 t/s, IQ2_S 101 t/s, IQ3_XXS 97 t/s, IQ3_S 90 t/s, **IQ3_S-mtp 150 t/s** with `--spec-type draft-mtp`.

This repository currently publishes 18 verified quant families as 144 GGUF shards. Five IQ families were produced with the V6 calibration importance matrix; the remaining families are standard core quantizations. Each family contains eight shards. Sizes below are the exact combined GGUF payload size; runtime memory also depends on context length, KV-cache format, offload configuration, and backend. “Yes” in the importance-matrix column means that `llama-quantize` was invoked with the V6 calibration importance matrix. Prompt processing was effectively unchanged in this run. RVN decoded 6.31% faster in this runtime, but the benchmark does **not** establish that the model edit caused the difference.

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> [!IMPORTANT] > **Generation is validated on one physical NVIDIA DGX Spark.** Use the pinned > Docker recipe below. Stock vLLM, stock ExLlamaV3, and a bare > `--tensor-parallel-size 1` command cannot load this TP4 rank-sliced artifact > correctly. This is a REAP-pruned, 3.0 bpw EXL3/Trellis build of `deepseek-ai/DeepSeek-V4-Flash-0731`, packaged for a **single 128 GB DGX Spark** through `local-inference-lab/sparkinfer`. - **Runtime repository:** `0xSero/deepseek-v4-flash-0731-spark-sparkinfer` - **Pinned public image:** `ghcr.io/0xsero/deepseek-v4-flash-0731-spark-sparkinfer@sha256:2e077489a83a0360952828051fe7f7a32c1801e5ce8436d85f7267583d614ff4` - **Pinned model revision:** `22f28d32b9b29b4352eaa380ff8c2c170b2847ab` - **API:** OpenAI-compatible server on `http://127.0.0.1:8000` - **Configured model limit:** 262,144 tokens