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rx1lora/Qwen3-4B-abliterated-llava-uncensored-lora

rx1lora Qwen 4B second-order
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Response includes
  • classification m1
  • files 8
  • author_summary 4 models
  • readme_text full
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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
2mo ago
created 2026-08-06
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Aug 5 → Oct 11 · 50 snapshots · spans 67 days

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Metadata

Tags
transformers safetensors generated_from_trainer trl sft base_model:huihui-ai/Qwen3-4B-abliterated base_model:finetune:huihui-ai/Qwen3-4B-abliterated endpoints_compatible region:us

Related

Total size
252 MB
Files
8
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-08-07 09:41

Files by quantization

Auxiliary files 8 files 263 MB
adapter_model.safetensors 252 MB 46374424 download
training_args.bin 5.70 KB eb2ec285 download
tokenizer.json 10.9 MB be756060 download
chat_template.jinja 4.02 KB 699ff8df download
.gitattributes 1.53 KB 52373fe2 download
README.md 1.49 KB b0064136 download
adapter_config.json 1.14 KB b4ef1566 download
tokenizer_config.json 378 B 23d7c669 download

README current version from Hugging Face


base_model: huihui-ai/Qwen3-4B-abliterated
library_name: transformers
model_name: Qwen3-4B-abliterated-llava-uncensored-lora
tags:

  • generated_from_trainer
  • trl
  • sft
    licence: license

Model Card for Qwen3-4B-abliterated-llava-uncensored-lora

This model is a fine-tuned version of huihui-ai/Qwen3-4B-abliterated.
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="rx1lora/Qwen3-4B-abliterated-llava-uncensored-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: 1.9.2
  • Transformers: 5.14.1
  • Pytorch: 2.11.0+cu128
  • Datasets: 5.0.1
  • Tokenizers: 0.22.2

Citations

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

README history 3 versions

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

  1. 2026-08-07Training in progress, step 4307fb7be61.5 KB
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  2. 2026-08-07Training in progress, step 107e64ed71.5 KB
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  3. 2026-08-06Training in progress, step 1007d81501.5 KB
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