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Koshkasa/Huihui-GLM-4.5-Air-abliterated-lossy-REAP_50-BF16-GGUF

Koshkasa Glm second-order
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Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
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Model age
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created 2026-10-22

Training datasets

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Variants by this author 2 formats · 0 downloads combined

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Metadata

License
mit
Languages
en
Tags
transformers text-generation en dataset:ChaoticNeutrals/Creative_Writing-ShareGPT dataset:hieunguyenminh/roleplay dataset:open-r1/Mixture-of-Thoughts dataset:theblackcat102/evol-codealpaca-v1 base_model:concedo/Huihui-GLM-4.5-Air-abliterated-lossytensors base_model:finetune:concedo/Huihui-GLM-4.5-Air-abliterated-lossytensors license:mit endpoints_compatible region:us

Related

Total size
0 B
Files
2
Quantizations
1
Registered
2026-10-22 16:00
Last updated on HF
2026-10-22 14:42

Files by quantization

Auxiliary files 2 files 2.44 KB
.gitattributes 1.48 KB a6344aac download
README.md 980 B 6d079bea download

README current version from Hugging Face


language:

  • en
    base_model:
  • concedo/Huihui-GLM-4.5-Air-abliterated-lossytensors
    library_name: transformers
    license: mit
    pipeline_tag: text-generation
    finetuned_by: Koshkasa
    base_model_relation: finetune
    datasets:
  • ChaoticNeutrals/Creative_Writing-ShareGPT
  • hieunguyenminh/roleplay
  • open-r1/Mixture-of-Thoughts
  • theblackcat102/evol-codealpaca-v1

This is a 50% REAP of lossytensors based on huihui's derestriction of GLM 4.5 Air, uploaded by concedo.

Since the previous (failed-ish) attempt the combined calibration dataset was generalized (while still having a 60%/40% creative bias) and re-wrapped on source basis. Activations were also recorded with higher length (8192 vs 3072).
As before, a sparse observer was utilized instead of materializing the entire [128, dim, tok] block when only top_k ones are actually routed.

IT IS CURRENTLY UNTESTED

It may not load, may produce gibberish, or not quantize. I'll address these if revealed during testing.

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