← back to catalog · registered 2026-08-22 13:56

tomvaillant/qwen3.6-27b-abliterated-journalist

tomvaillant Qwen 27B second-order
Your rig guess connected
? Why do I need an app?
Reading your rig…

This is a rough estimate. Install the free app - we'll show exact numbers.

Reading real hardware from your app right now. Numbers below are exact.

Below is the per-quantization compatibility for this model.

curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/tomvaillant%2Fqwen3.6-27b-abliterated-journalist"
Response includes
  • classification m1
  • files 8
  • hub_downloads_all_time 29
  • author_summary 5 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
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)
Downloads · lifetime
29
Likes
0
Model age
4mo ago
created 2026-05-22
Downloads over time
Now29→from29↑0%
2929303029 on May 2029 on Oct 11MayJunJulAugSepOct
May 20 → Oct 11 · 60 snapshots · spans 144 days

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Variants by this author 2 formats · 229 downloads combined

The same weights this author released in different packaging. Pick the format that matches your runtime.

Metadata

License
apache-2.0
Languages
en
Tags
transformers safetensors text-generation-inference unsloth qwen3_5 trl investigative-journalism osint lora adapter conversational en

Related

Total size
445 MB
Files
8
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-06-17 12:00

Files by quantization

Auxiliary files 8 files 465 MB
adapter_model.safetensors 445 MB b95850d7 download
tokenizer.json 19.1 MB 87a7830d download
chat_template.jinja 7.58 KB a8755d82 download
tokenizer_config.json 6.99 KB f63477f5 download
README.md 2.97 KB 803eed48 download
adapter_config.json 1.60 KB 8c7422fc download
.gitattributes 1.53 KB 52373fe2 download
processor_config.json 1.16 KB 33818c7f download

README current version from Hugging Face


license: apache-2.0
base_model: huihui-ai/Huihui-Qwen3.6-27B-abliterated
language:

  • en
    tags:
  • transformers
  • text-generation-inference
  • unsloth
  • qwen3_5
  • trl
  • investigative-journalism
  • osint
  • lora
  • adapter
  • conversational

qwen3.6-27b-abliterated-journalist

LoRA adapter for investigative journalism and OSINT workflows, fine-tuned from huihui-ai/Huihui-Qwen3.6-27B-abliterated.

This is the adapter checkpoint. For local inference, use the merged or GGUF variants:

Training

  • Method: LoRA with Unsloth FastModel + TRL SFT, following the official Unsloth Qwen3.5 fine-tune recipe (canonical for Qwen3.6 too).
  • Base model: huihui-ai/Huihui-Qwen3.6-27B-abliterated (multimodal, hybrid-thinking, dense 27B, qwen3_5 architecture)
  • Dataset: tomvaillant/investigative-journalism-training (687 examples, OSINT methodology)
  • LoRA config: r=16, alpha=16, dropout=0; targets q/k/v/o/gate/up/down projections; bias="none"; use_gradient_checkpointing="unsloth"
  • Precision: bf16 (load_in_16bit=True; 4-bit QLoRA explicitly not recommended for Qwen3.5/3.6 per Unsloth docs)
  • Optimizer: adamw_8bit, lr 2e-4, warmup 10 steps, 2 epochs, effective batch = 1 × 4 grad-accum
  • Vision tower: frozen (finetune_vision_layers=False) — text-only LoRA, vision capability preserved byte-identical to base
  • Final loss: 0.52 (step 300, epoch ~1.75)
  • Task: investigative reporting assistance, OSINT methodology, verification, public-records research, source handling, and ethics

Sources And Attribution

Training data: tomvaillant/investigative-journalism-training — 687 instruction/response pairs synthesized by Claude Opus 4.6 (Anthropic) from the Buried Signals OSINT and investigative-journalism corpus: OSINT Navigator tool data, Indicator Media briefings, Buried Signals investigative skills, GIJN, Bellingcat, Verification Handbook 3, SPJ Code of Ethics, RCFP, and public manuals from UNESCO, Al Jazeera Media Institute, CiFAR, CIPE, and EJF/TEMPO Institute.

See the dataset card for the full source list, licenses, and per-partner attribution.

Intended Use

This model is intended for journalist-facing assistant workflows: investigation planning, OSINT tool selection, verification checklists, public-source research methods, and evidence-grounded drafting. Verify model outputs before use in reporting.

This was trained with Unsloth.

README history 4 versions

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

  1. 2026-06-17Point attribution to dataset card (single source of truth)ea617f43 KB
    Loading...
  2. 2026-05-25docs: expand SOURCES attribution (Cached Q&A, Amditis, FTM primary sources, n...03ce89d5 KB
    Loading...
  3. 2026-05-23Update README: accurate recipe + full source attribution15ba4f53 KB
    Loading...
  4. 2026-05-22Upload trained adapter (attempt 1)9fa84c55.1 KB
    Loading...
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration