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Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored

Krypto-Whitehat Qwen 9B GGUF 262K ctx
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curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/Krypto-Whitehat%2Fqwen3.8-9b-cyber-exploit-agent-uncensored"
Response includes
  • classification m-uncensored
  • files 9
  • hub_downloads_all_time 2,473
  • author_summary 3 models
  • readme_text full
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What "m-uncensored" means · taxonomy
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Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
How we label → About M-U →
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

What is a refusal direction? →
Downloads · lifetime
2K
1K last 30d - stable
Likes
2
Model age
7w ago
created 2026-08-17
Downloads over time
Now2.7K→from1K↑161%
9591.6K2.2K2.9K1K on Aug 192.7K on Oct 11AugSepOct
Aug 19 → Oct 11 · 48 snapshots · spans 53 days

Metadata

Quantizations
Q4_K Q5_K
Tags
gguf endpoints_compatible region:us conversational

Related

Other by Krypto-Whitehat
qwen3.8-9b-uncensored-cyber-exploit-XRPL-v3 2K qwen3.8-9b-uncensored-cyber-xrpl-v4 250
Total size
11.6 GB
Files
9
Quantizations
3
Registered
2026-08-22 13:56
Last updated on HF
2026-08-17 22:37
Source huggingface.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent-uncensored ↗

Files by quantization

Q5_K 1 file 6.19 GB
Qwen3.8-9B-Cyber-Exploit-UNCENSORED-Q5_K_M.gguf 6.19 GB 9602312d download
Q4_K 1 file 5.38 GB
Qwen3.8-9B-Cyber-Exploit-UNCENSORED-Q4_K_M.gguf 5.38 GB 621472ef download
Auxiliary files 7 files 3.72 MB
train_all_v2_shipped.jsonl 2.13 MB 86bf866a download
trackA.jsonl 1.54 MB 47bba006 download
train_ids.json 25.9 KB 2f931e56 download
eval_ids.json 17.8 KB 471fb18a download
.gitattributes 1.65 KB be841a60 download
inference_system.txt 1.64 KB 7f732ab4 download
README.md 1013 B c5ecc1e2 download

README current version from Hugging Face

Training Data — qwen3.8-9b-cyber-exploit-agent

This is the exact dataset the shipped model was trained on (QLoRA r16/a16, 3 epochs, best ckpt by eval loss).

  • train_all_v2_shipped.jsonl — 395 samples: 280 CyberGym train-config tasks (8 blacklisted oss-fuzz IDs removed, Elfsong eval-200 never trained on) + 33 XRPL samples x3 (code-verified gates F1-F22/D/E/N, real issue texts, no maintainer comments in user turns) + 16 own labs/boundary samples.
  • trackA.jsonl — Track A source samples (280).
  • labs/ + evidence/ — 14 locally compiled and triggered labs (ASan logs, Python RCE markers). No invented crashes.
  • scripts/ — full reproducible pipeline (dataset builders, SFT, merge, GGUF chain, eval gates).
  • inference_system.txt — the training system prompt; use it at inference.
  • train_ids.json / eval_ids.json — task id lists (train minus blacklist / eval holdout).

Dataset gate at build time: 0 blacklist ids, 0 user-turn leak markers, 0 schema violations, G1/G2/G6/G7/G8 verdicts pinned.

README history 2 versions

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

  1. 2026-08-17Upload folder using huggingface_hub4bb27f01013 B
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  2. 2026-08-17Upload README.md with huggingface_hub0e9df902 KB
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