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electroglyph/LFM2.5-24B-A2B-uncensored-fiction-nblora-merged

electroglyph 24B second-order
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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
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created 2026-11-03

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Metadata

Tags
transformers safetensors generated_from_trainer trl nblora base_model:electroglyph/LFM2.5-24B-A2B-uncensored base_model:finetune:electroglyph/LFM2.5-24B-A2B-uncensored endpoints_compatible region:us

Related

Total size
0 B
Files
2
Quantizations
1
Registered
2026-11-03 17:02
Last updated on HF
2026-11-03 19:07

Files by quantization

Auxiliary files 2 files 3.05 KB
.gitattributes 1.61 KB aee27c0a download
README.md 1.43 KB 9bdf1f9c download

README current version from Hugging Face


base_model: electroglyph/LFM2.5-24B-A2B-uncensored
library_name: transformers
model_name: output-fiction2-nblora
tags:

  • generated_from_trainer
  • trl
  • nblora
    licence: license

Model Card for output-fiction2-nblora

This model is a fine-tuned version of electroglyph/LFM2.5-24B-A2B-uncensored.
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="None", device_map="auto")
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: 1.18.0.dev0
  • Transformers: 5.22.1
  • Pytorch: 2.15.0
  • Datasets: 5.1.0
  • Tokenizers: 0.23.3

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}
}
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