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

alexgusevski/Llama-3.3-8B-Instruct-128K_Abliterated-mlx-6Bit

alexgusevski Llama 8.0B 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/alexgusevski%2FLlama-3.3-8B-Instruct-128K_Abliterated-mlx-6Bit"
Response includes
  • classification m1
  • files 11
  • benchmarks 11 entries
  • hub_downloads_all_time 242
  • author_summary 64 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
242
25 last 30d - stable
Likes
0
Model age
9mo ago
created 2026-01-12
Downloads over time
Now247→from34↑626%
2310518726834 on Jan 14247 on Oct 11247 on Oct 8JanMarMayJulSep
Jan 14 → Oct 11 · 78 snapshots · spans 270 days

Benchmarks

Benchmark Score Source
Entertainment 1.7 UGI
Hazardous 1.8 UGI
Natural Intelligence 16.56 UGI
Political lean -13.1% UGI
Sensitive-Info 19.78 UGI
SocPol 2.4 UGI
UGI 39.02 UGI
Willingness (10) 7.8 UGI
W10-Adherence 7.5 UGI
W10-Direct 8 UGI
Writing 12.27 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
llama3.3
Languages
en
Tags
mlx safetensors llama en base_model:SicariusSicariiStuff/Llama-3.3-8B-Instruct-128K_Abliterated base_model:quantized:SicariusSicariiStuff/Llama-3.3-8B-Instruct-128K_Abliterated license:llama3.3 6-bit region:us

Related

Total size
6.08 GB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-01-12 13:15

Files by quantization

Auxiliary files 11 files 6.09 GB
model-00001-of-00002.safetensors 4.97 GB 250f90bf download
model-00002-of-00002.safetensors 1.10 GB 783b74e0 download
tokenizer.json 16.4 MB 8fc5ed64 download
model.safetensors.index.json 62.1 KB f64ef4b3 download
tokenizer_config.json 49.5 KB 6e9b7ecb download
chat_template.jinja 4.51 KB e76734d6 download
.gitattributes 1.53 KB 52373fe2 download
README.md 1.27 KB ee4ff8cb download
config.json 1.09 KB b741c367 download
special_tokens_map.json 439 B 344c8261 download
generation_config.json 208 B 74d5f8f4 download

README current version from Hugging Face


license: llama3.3
language:


alexgusevski/Llama-3.3-8B-Instruct-128K_Abliterated-mlx-6Bit

The Model alexgusevski/Llama-3.3-8B-Instruct-128K_Abliterated-mlx-6Bit was converted to MLX format from SicariusSicariiStuff/Llama-3.3-8B-Instruct-128K_Abliterated using mlx-lm version 0.29.1.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("alexgusevski/Llama-3.3-8B-Instruct-128K_Abliterated-mlx-6Bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

README history 1 version

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

  1. 2026-01-12Upload README.md with huggingface_hubcf918641.3 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