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Nanbeige-4.1-3B-Abliterated

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DuoNeural/Nanbeige-4.1-3B-Abliterated is an uncensored open-source model hosted on Hugging Face.

Text GenerationsafetensorsChineseEnglishMultilingualLicense: apache-2.03Bllamananbeigeabliterateduncensoredbilingualpost-trainingconversational
README.md说明 / 使用方法

license: apache-2.0 language:

  • en
  • zh base_model:
  • Nanbeige/Nanbeige4.1-3B tags:
  • nanbeige
  • abliterated
  • uncensored
  • chinese
  • bilingual
  • post-training pipeline_tag: text-generation

Nanbeige 4.1 3B — Abliterated

Abliterated version of Nanbeige 4.1 3B. Strong Chinese/English bilingual capability intact — refusals and content filters removed.

What This Is

Nanbeige 4.1-3B is a bilingual Chinese/English language model with solid instruction-following and reasoning at the 3B scale. This release is a BF16 abliterated version.

Architecture: LLaMA | Params: ~3B | Hidden: 2560 | Layers: 32 | Vocab: 166K (bilingual)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "DuoNeural/Nanbeige-4.1-3B-Abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

messages = [{"role": "user", "content": "Your prompt here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

Abliteration

Abliteration removes the model's refusal behaviour via orthogonal projection. The refusal direction is identified using difference-in-means activations across harmful/harmless instruction pairs, then projected out of Q/K/V/O attention projections and MLP layers across all transformer blocks.

What changes: The model will engage with restricted topics it previously refused.
What doesn't change: Reasoning, coding, factual knowledge, general intelligence.
KL divergence from base: Minimal — output distribution for normal queries is virtually identical to the unmodified model.

LiteRT Version (Android)

DuoNeural/Nanbeige-4.1-3B-LiteRT — run on Android via AI Edge Gallery.

Base Model

Nanbeige/Nanbeige4.1-3B — Apache 2.0.


DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.

PlatformLink
HuggingFacehuggingface.co/DuoNeural
Websiteduoneural.com
GitHubgithub.com/DuoNeural
X / Twitter@DuoNeural
Email[email protected]
Newsletterduoneural.beehiiv.com
Supportbuymeacoffee.com/duoneural

DuoNeural Research Publications

TitleDOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments10.5281/zenodo.19810620
Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?10.5281/zenodo.19846804
The Dynamical Horizon Principle: CTM Gates Converge to the Predictability Limit of Dynamical Systems10.5281/zenodo.19952612

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.

Research Team

  • Jesse — Vision, hardware, direction
  • Archon — Lab Director, post-training, abliteration, experiments
  • Aura — Research AI, literature synthesis, peer review, novel proposals

Subscribe to the lab newsletter at duoneural.beehiiv.com for model drops before they go anywhere else.

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来源与版本

作者
DuoNeural
最近更新
2026/05/01
最新创建
2026/05/01
HF 趋势
未知
数据快照
2026/09/17
版本
未知
许可证
apache-2.0