Ornstein-Hermes-3.6-27B SABER GGUF

GGUF quantizations of GestaltLabs/Ornstein-Hermes-3.6-27b-SABER, a SABER-edited version of GestaltLabs/Ornstein-Hermes-3.6-27b.

SABER is a controlled refusal-shaping workflow. The release target is to reduce broad over-refusal while preserving ordinary model behavior and visible boundaries for severe criminal, coercive, or interpersonal-harm requests. The selected checkpoint was chosen as a Pareto point over refusal rate and behavioral drift.

Source Checkpoint

The one retained refusal in the expanded evaluation was an illegal-drug-sales request. This is an observed result on the current evaluation set, not a universal guarantee about future behavior.

Quantization Files

The included imatrix file was generated from DJLougen/Acta-Synthetic. It is included for reproducibility and for users who want to regenerate adjacent quantizations.

Recommended File

Start with for normal desktop use. Use or if you have enough VRAM/RAM and want a higher-quality local run. Use when file size matters more. is mainly for high-memory systems or as a near-lossless GGUF reference.

llama.cpp Compatibility

These files were produced with llama.cpp commit from a BF16 GGUF conversion of the SABER checkpoint. The model uses the GGUF architecture path in current llama.cpp.

Example:

For chat-style use, prefer a frontend or wrapper that applies the tokenizer chat template from the GGUF metadata.

Conversion and Quantization Notes

The Q8_0 GGUF was converted from the full SABER Hugging Face checkpoint. The lower-bit recovery quants were generated from the published Q8_0 GGUF with --allow-requantize and the included Acta-Synthetic imatrix so the missing files could be restored quickly. Importance-matrix calibration used Acta-Synthetic conversational text.

Method Summary

SABER edits refusal behavior through activation/weight-space refusal directions. For this checkpoint, the run used SVD extraction, multi-layer candidate selection, iterative ablation, and KLD-based drift measurement.

Run configuration:

Selected layers:

Total directions ablated: .

Attribution and Related Work

This release builds on the refusal-direction and abliteration research lineage. Relevant prior work and inspirations include:

• Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda, Refusal in Language Models Is Mediated by a Single Direction, 2024.
• Maxime Labonne, Uncensor any LLM with abliteration, 2024.
• FailSpy, abliterator, and associated abliterated model releases.
• Jim Lai (), Projected Abliteration, 2025, and Norm-Preserving Biprojected Abliteration, 2025.
• Philipp Emanuel Weidmann, Heretic, 2025-2026.
• Pliny the Prompter / OBLITERATUS, Hugging Face Space and OBLITERATUS releases.
• Jiunsong, SuperGemma4 E4B Abliterated, and related SuperGemma releases.
• Jiachen Zhao, Jing Huang, Zhengxuan Wu, David Bau, and Weiyan Shi, LLMs Encode Harmfulness and Refusal Separately, 2025.

SABER's contribution in this release is the controlled-refusal-shaping workflow: multi-candidate refusal extraction, separability/entanglement-aware ranking, differential ablation strength, and explicit Pareto selection over refusal behavior and KLD drift.

Limitations

• Results are specific to the current evaluation set, prompts, and generation settings.
• The KLD value should be interpreted relative to the base-vs-base control, not as an absolute standalone score.
• Quantization changes numerical behavior; validate the specific GGUF file you deploy.
• The model inherits constraints, limitations, and licensing considerations from the base model.
• This is a model-editing research artifact with dual-use implications.