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DuoNeural/Nanbeige-4.1-3B-Abliterated

DuoNeural Llama 3.9B
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Response includes
  • classification m1
  • files 13
  • benchmarks 11 entries
  • hub_downloads_all_time 85
  • author_summary 45 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)
Downloads · lifetime
85
17 last 30d - stable
Likes
0
Descendants
2
in 2 direct forks
Model age
5mo ago
created 2026-05-01
Downloads over time
Now88→from24↑267%
2145709424 on Apr 2988 on Oct 1188 on Oct 9AprMayJunJulAugSepOct
Apr 29 → Oct 11 · 63 snapshots · spans 165 days

Benchmarks

Portrait before abliteration
Benchmarks of the base model as it stood before the refusal-removal operation. Compare with the numbers above to see what the operation cost.
Benchmark Score Source
Entertainment 0.6 UGI
Hazardous 1.2 UGI
Natural Intelligence 10.69 UGI
Political lean -23.2% UGI
Sensitive-Info 9.9 UGI
SocPol 1.3 UGI
UGI 16.6 UGI
Willingness (10) 3 UGI
W10-Adherence 1 UGI
W10-Direct 5 UGI
Writing NA UGI

Genealogy 2 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

License
apache-2.0
Languages
en zh
Tags
safetensors llama nanbeige abliterated uncensored chinese bilingual post-training text-generation conversational en zh

Related

Total size
7.33 GB
Files
13
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-05-01 16:59

Files by quantization

Auxiliary files 13 files 7.35 GB
model-00001-of-00002.safetensors 4.64 GB 2851e24f download
model-00002-of-00002.safetensors 2.69 GB d455c9b5 download
tokenizer.json 17.6 MB 5cadb0fa download
tokenizer.model 2.65 MB fb41d047 download
model.safetensors.index.json 23.4 KB 92865eab download
chat_template.jinja 5.40 KB 6a8aa8cb download
README.md 4.09 KB 4ae37fd1 download
tokenizer_config.json 2.25 KB 65ed3089 download
.gitattributes 1.53 KB 52373fe2 download
config.json 749 B da31cd8e download
special_tokens_map.json 623 B fb83292a download
added_tokens.json 174 B 8805f6dd download
generation_config.json 142 B 6ea8072d download

README current version from Hugging Face


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.

Platform Link
HuggingFace huggingface.co/DuoNeural
Website duoneural.com
GitHub github.com/DuoNeural
X / Twitter @DuoNeural
Email [email protected]
Newsletter duoneural.beehiiv.com
Support buymeacoffee.com/duoneural

DuoNeural Research Publications

Title DOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning 10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments 10.5281/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 Systems 10.5281/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

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README history 1 version

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

  1. 2026-05-01Add model card with DuoNeural standard footerd01f8624.1 KB
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