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ikarius/NeuralDaredevil-8B-abliterated-NF4

ikarius Llama 7.2B second-order
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Response includes
  • classification m1
  • files 11
  • benchmarks 16 entries
  • hub_downloads_all_time 97
  • author_summary 17 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
97
23 last 30d - stable
Likes
1
Model age
10mo ago
created 2025-11-15
Downloads over time
Now105→from15↑600%
11458011415 on Nov 19, 2025105 on Oct 11Nov '25JanMarMayJulSep
Nov 19, 2025 → Oct 11 · 86 snapshots · spans 326 days

Benchmarks

Benchmark Score Source
BBH average 0.4680243870800363 OpenLLM-v2
IFEval instruct 0.7949640287769785 OpenLLM-v2
IFEval-Prompt 0.7171903881700554 OpenLLM-v2
MATH lvl 5 0.08006042296072508 OpenLLM-v2
MMLU-Pro 0.38414228723404253 OpenLLM-v2
Entertainment 1 UGI
Hazardous 2.9 UGI
Natural Intelligence 19.28 UGI
Political lean -13.8% UGI
Sensitive-Info 17.3 UGI
SocPol 1.6 UGI
UGI 33.2 UGI
Willingness (10) 6.5 UGI
W10-Adherence 7 UGI
W10-Direct 6 UGI
Writing 24.86 UGI

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

License
llama3
Languages
en multilingual
Tags
safetensors llama llama3 8b abliteration uncensored nf4 4bit quantized bitsandbytes text-generation conversational

Related

Total size
5.31 GB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-12-15 15:21

Files by quantization

Auxiliary files 11 files 5.33 GB
model-00001-of-00002.safetensors 4.33 GB 4876913d download
model-00002-of-00002.safetensors 1002 MB 2c17b8e3 download
tokenizer.json 16.4 MB 3c5cf440 download
model.safetensors.index.json 129 KB 97a3e287 download
tokenizer_config.json 49.4 KB e079bc08 download
README.md 1.85 KB 56b2df41 download
.gitattributes 1.53 KB 52373fe2 download
config.json 1.14 KB 12daaee7 download
chat_template.jinja 389 B 39bd0c9f download
special_tokens_map.json 301 B cfabacc2 download
generation_config.json 194 B 73eb4953 download

README current version from Hugging Face


license: llama3
base_model: mlabonne/NeuralDaredevil-8B-abliterated
tags:

  • llama
  • llama3
  • 8b
  • abliteration
  • uncensored
  • nf4
  • 4bit
  • quantized
  • safetensors
  • bitsandbytes
    language:
  • en
  • multilingual
    pipeline_tag: text-generation

NeuralDaredevil-8B-abliterated-NF4

Permanently quantized to NF4

This is a 4-bit NF4 quantized version of:
→ mlabonne/NeuralDaredevil-8B-abliterated
(fine-tuned & abliteration by mlabonne)

License: Meta Llama 3 Community License
Same as base model – commercial use allowed.


Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ikarius/NeuralDaredevil-8B-abliterated-NF4"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,   # Optimal for Llama 3
    trust_remote_code=True
)

input_text = "Hva er abliteration?"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    do_sample=True,
    temperature=0.7,
    top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Quantization Details

'''

  • Original model: mlabonne/NeuralDaredevil-8B-abliterated
  • Architecture: Llama 3 8B
  • Quantization: bitsandbytes 4-bit NF4 (permanent)
  • Double quantization: Enabled
  • Compute dtype: bfloat16 ← recommended for Llama 3
  • Estimated VRAM: ~6.3–6.7 GB (CUDA)
    '''

Credits

Base model & abliteration: @mlabonne
NF4 quantization: @ikarius

License
Same as base model: llama3

README history 4 versions

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

  1. 2025-12-15Update README.mdd96b02b1.9 KB
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  2. 2025-11-15Update README.mdaa0133c1.9 KB
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  3. 2025-11-15Update README.mdd82fe111.9 KB
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  4. 2025-11-15initial commitfda196524 B
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