← back to catalog · registered 2026-08-29 03:03

nuofang/Qwen3.5-9B-Distill-Bradbury-F451-SLERP-Uncensored-GGUF

nuofang Qwen 9B second-order
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Response includes
  • classification m5
  • files 5
  • author_summary 22 models
  • readme_text full
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Abliteration classifier · v1.0.0
M5
Primary method

Mergekit merge

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.
  • merge tag / mergekit / dare-ties in tags or name
  • no unusual architecture pattern (regular merge)
  • no abliteration marker
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No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

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Downloads · 30-day
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Likes
0
Model age
8w ago
created 2026-08-29

Genealogy 0 direct forks

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Metadata

Quantizations
Q5_K
Tags
gguf llama.cpp quantized imatrix base_model:nuofang/Qwen3.5-9B-Distill-Bradbury-F451-SLERP-Uncensored base_model:quantized:nuofang/Qwen3.5-9B-Distill-Bradbury-F451-SLERP-Uncensored region:us

Related

Total size
6.03 GB
Files
5
Quantizations
3
Registered
2026-08-29 03:03
Last updated on HF
2026-08-29 03:29

Files by quantization

Q5_K 1 file 6.02 GB
Qwen3.5-9B-Distill-Bradbury-F451-SLERP-Uncensored-Q5_K_M-no-mtp.gguf 6.02 GB 0b899e18 download
F16 1 file 879 MB
mmproj-Qwen3.5-9B-Distill-Bradbury-F451-SLERP-Uncensored-f16.gguf 879 MB b77ecf23 download
Auxiliary files 3 files 4.91 MB
imatrix.gguf 4.91 MB 468ac439 download
.gitattributes 1.73 KB 07e07036 download
README.md 1.25 KB a4ab7c5f download

README current version from Hugging Face


base_model: nuofang/Qwen3.5-9B-Distill-Bradbury-F451-SLERP-Uncensored
tags:

  • llama.cpp
  • quantized
  • imatrix

Auto-Quantized GGUF Model

This repository contains automated GGUF quantization files for nuofang/Qwen3.5-9B-Distill-Bradbury-F451-SLERP-Uncensored.

The calibration data for the imatrix is targeted at Chinese novels and role-playing (RP), while preserving logic and common sense.
imatrix 的校准数据以中文的小说、角色扮演为目标,同时保留逻辑和常识。

If the perplexity drops after quantization compared to the original precision, it might not actually be an improvement. Instead, it could be caused by differences in how llama.cpp quantization and perplexity tools handle special tokens. I will update the README generation code in the future.
如果困惑度在量化之后与原精度相比变低,可能并不是真的提升,而是llamacpp量化工具和困惑度计算工具处理特殊token行为不同导致的,我将在未来修改生成readme的代码。

Perplexity Evaluation

(Tested against the provided calibration dataset)

  • Base (F16/BF16): PPL = 14.1462 +/- 0.11497
  • Q5_K_M: PPL = 12.0146 +/- 0.09496

README history 14 versions

The author's README evolved over time. Click a version to see its content at that point.

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Discussions 1 thread

  1. 2026-09-13Plans for bigger model?open3 💬#1
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