← back to catalog · registered 2026-11-05 13:02

electroglyph/LFM2.5-24B-A2B-uncensored-fiction-nblora-merged2

electroglyph 24B 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/electroglyph%2FLFM2.5-24B-A2B-uncensored-fiction-nblora-merged2"
Response includes
  • classification unknown
  • files 2
  • author_summary 17 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
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
today
created 2026-11-05

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
region:us

Related

Total size
0 B
Files
2
Quantizations
1
Registered
2026-11-05 13:02
Last updated on HF
2026-11-05 13:01

Files by quantization

Auxiliary files 2 files 3.26 KB
README.md 1.64 KB edd9fe40 download
.gitattributes 1.61 KB e9998c70 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

Visualize in Weights & Biases

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}
}
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