← back to catalog · registered 2026-08-22 13:56

RichardErkhov/huihui-ai_-_Llama-3.2-3B-Instruct-abliterated-awq

RichardErkhov Llama 2.8B
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/RichardErkhov%2Fhuihui-ai_-_Llama-3.2-3B-Instruct-abliterated-awq"
Response includes
  • classification m1
  • files 8
  • hub_downloads_all_time 213
  • author_summary 257 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
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
213
19 last 30d - cooling
Likes
0
Model age
22mo ago
created 2024-11-27
Downloads over time
Now227→from2↑11,250%
0831662502 on Nov 27, 2024227 on Oct 12Nov '24Feb '25May '25Aug '25Nov '25FebMayAug
Nov 27, 2024 → Oct 12 · 138 snapshots · spans 684 days

Variants by this author 2 formats · 2K downloads combined

The same weights this author released in different packaging. Pick the format that matches your runtime.

Metadata

Tags
safetensors llama 4-bit awq region:us

Related

Total size
2.10 GB
Files
8
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2024-11-27 16:08

Files by quantization

Auxiliary files 8 files 2.11 GB
model.safetensors 2.10 GB 6930d2c1 download
tokenizer.json 16.4 MB 6b9e4e7f download
tokenizer_config.json 53.4 KB 610743cb download
README.md 2.70 KB 1ff3037b download
.gitattributes 1.53 KB 52373fe2 download
config.json 1.08 KB fe31e2d1 download
special_tokens_map.json 439 B 344c8261 download
generation_config.json 184 B 144bd5f2 download

README current version from Hugging Face

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Llama-3.2-3B-Instruct-abliterated - AWQ

Original model description:

library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:

  • abliterated
  • uncensored

🦙 Llama-3.2-3B-Instruct-abliterated

This is an uncensored version of Llama 3.2 3B Instruct created with abliteration (see this article to know more about it).

Special thanks to @FailSpy for the original code and technique. Please follow him if you're interested in abliterated models.

ollama

You can use huihui_ai/llama3.2-abliterate:3b directly,

ollama run huihui_ai/llama3.2-abliterate

or create your own model using the following methods.

  1. Download this model.
huggingface-cli download huihui-ai/Llama-3.2-3B-Instruct-abliterated --local-dir ./huihui-ai/Llama-3.2-3B-Instruct-abliterated
  1. Get Llama-3.2-3B-Instruct model for reference.
ollama pull llama3.2
  1. Export Llama-3.2-3B-Instruct model parameters.
ollama show llama3.2 --modelfile > Modelfile
  1. Modify Modelfile, Remove all comment lines (indicated by #) before the "FROM" keyword. Replace the "FROM" with the following content.
FROM huihui-ai/Llama-3.2-3B-Instruct-abliterated
  1. Use ollama create to then create the quantized model.
ollama create --quantize q4_K_M -f Modelfile Llama-3.2-3B-Instruct-abliterated-q4_K_M
  1. Run model
ollama run Llama-3.2-3B-Instruct-abliterated-q4_K_M

The running architecture is llama.

Evaluations

The following data has been re-evaluated and calculated as the average for each test.

Benchmark Llama-3.2-3B-Instruct Llama-3.2-3B-Instruct-abliterated
IF_Eval 76.55 76.76
MMLU Pro 27.88 28.00
TruthfulQA 50.55 50.73
BBH 41.81 41.86
GPQA 28.39 28.41

The script used for evaluation can be found inside this repository under /eval.sh, or click here

README history 1 version

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

  1. 2024-11-27uploaded readme3aaf0442.7 KB
    Loading...
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