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

cognitron/my-jailbreak-model-lora

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/cognitron%2Fmy-jailbreak-model-lora"
Response includes
  • classification unknown
  • files 11
  • author_summary 2 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
?
Primary method

Unclassified

No clear signals of an abliteration technique in this model.
Confidence
UNKNOWN
Why this label 1 signal
No classification signals present. This may not be an abliterated model at all - it could be a repackaging, a merge with unrelated goals, or unrelated content that mentions the term.
  • no classification signals present (no abliterated, uncensored, or known producer/method markers)
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 · 30-day
0
Likes
0
Model age
9mo ago
created 2025-12-29
Downloads over time
Now0→from0↑0%
00110 on Dec 31, 20250 on Oct 11Dec '25FebAprJunAugOct
Dec 31, 2025 → Oct 11 · 80 snapshots · spans 284 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.

Metadata

Tags
transformers safetensors generated_from_trainer trl unsloth sft endpoints_compatible region:us

Related

Total size
192 MB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-12-29 02:15

Files by quantization

Auxiliary files 11 files 196 MB
adapter_model.safetensors 192 MB d542bb65 download
training_args.bin 6.20 KB bd07ff12 download
tokenizer.json 3.45 MB 21b7029f download
tokenizer.model 488 KB 9e556afd download
tokenizer_config.json 2.87 KB 61a6e926 download
README.md 1.51 KB 982f9e0b download
.gitattributes 1.48 KB a6344aac download
adapter_config.json 1.13 KB 3a4b2755 download
special_tokens_map.json 572 B 24f8988c download
chat_template.jinja 407 B ddb5006b download
added_tokens.json 293 B c9d3d3a1 download

README current version from Hugging Face


base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit
library_name: transformers
model_name: my-jailbreak-model-lora
tags:

  • generated_from_trainer
  • trl
  • unsloth
  • sft
    licence: license

Model Card for my-jailbreak-model-lora

This model is a fine-tuned version of unsloth/phi-3-mini-4k-instruct-bnb-4bit.
It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="cognitron/my-jailbreak-model-lora", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

  • TRL: 0.24.0
  • Transformers: 4.57.3
  • Pytorch: 2.9.0+cu126
  • Datasets: 4.3.0
  • Tokenizers: 0.22.1

Citations

Cite TRL as:

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}

README history 1 version

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

  1. 2025-12-29Training in progress, step 216ae19a01.5 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