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

mlx-community/phi-4-abliterated-3bit

mlx-community Phi 15B 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/mlx-community%2Fphi-4-abliterated-3bit"
Response includes
  • classification m1
  • files 11
  • hub_downloads_all_time 334
  • author_summary 207 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
334
32 last 30d - cooling
Likes
0
Model age
21mo ago
created 2025-01-09
Downloads over time
Now354→from14↑2,429%
012925938814 on Jan 8, 2025354 on Oct 11Jan '25Apr '25Jul '25Oct '25JanAprJulOct
Jan 8, 2025 → Oct 11 · 131 snapshots · spans 641 days

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
mit
Languages
en
Tags
transformers safetensors phi3 text-generation phi nlp math code chat conversational abliterated uncensored

Related

Total size
5.97 GB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-01-09 18:35

Files by quantization

Auxiliary files 11 files 5.98 GB
model-00001-of-00002.safetensors 4.96 GB 9989848d download
model-00002-of-00002.safetensors 1.02 GB f9e051b7 download
tokenizer.json 6.82 MB 624ba298 download
vocab.json 1.54 MB c5dc46ce download
merges.txt 895 KB 354558ed download
model.safetensors.index.json 48.1 KB fad0eb62 download
tokenizer_config.json 17.3 KB 1c9ae385 download
.gitattributes 1.48 KB a6344aac download
README.md 1.22 KB 61c99852 download
config.json 996 B 8cc7d7d4 download
special_tokens_map.json 579 B e3bbb9ed download

README current version from Hugging Face


license: mit
license_link: https://huggingface.co/huihui-ai/phi-4-abliterated/resolve/main/LICENSE
language:

  • en
    base_model: huihui-ai/phi-4-abliterated
    pipeline_tag: text-generation
    tags:
  • phi
  • nlp
  • math
  • code
  • chat
  • conversational
  • abliterated
  • uncensored
  • mlx
  • mlx-my-repo
    inference:
    parameters:
    temperature: 0
    widget:
  • messages:
    • role: user
      content: How should I explain the Internet?
      library_name: transformers

mlx-community/phi-4-abliterated-3bit

The Model mlx-community/phi-4-abliterated-3bit was converted to MLX format from huihui-ai/phi-4-abliterated using mlx-lm version 0.20.5.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/phi-4-abliterated-3bit")

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 2 versions

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

  1. 2025-01-09Update README.mdf8b0c7c1.2 KB
    Loading...
  2. 2025-01-09Upload README.md with huggingface_hub23536c71.2 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