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

SnowFlash383935/gemma-3-4b-it-abliterated-mlx-6Bit

SnowFlash383935 Gemma 4.6B multimodal 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/SnowFlash383935%2Fgemma-3-4b-it-abliterated-mlx-6Bit"
Response includes
  • classification m1
  • files 10
  • hub_downloads_all_time 470
  • author_summary 4 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
470
15 last 30d - cooling
Likes
0
Model age
14mo ago
created 2025-07-20
Downloads over time
Now478→from90↑431%
7121936851790 on Jul 23, 2025478 on Oct 12Jul '25Sep '25Nov '25JanMarMayJulSep
Jul 23, 2025 → Oct 12 · 104 snapshots · spans 446 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
gemma
Tags
transformers safetensors gemma3 image-text-to-text abliterated uncensored mlx mlx-my-repo conversational license:gemma text-generation-inference endpoints_compatible

Related

Total size
3.44 GB
Files
10
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-07-20 08:48

Files by quantization

Auxiliary files 10 files 3.48 GB
model.safetensors 3.44 GB ea8a1df7 download
tokenizer.json 31.8 MB 4667f208 download
tokenizer.model 4.47 MB 1299c11d download
tokenizer_config.json 1.10 MB 7aa1c868 download
model.safetensors.index.json 78.0 KB 255c9f8a download
.gitattributes 1.53 KB 52373fe2 download
README.md 1.36 KB 1a939fd4 download
config.json 928 B 2870bb1a download
special_tokens_map.json 662 B d09a9d23 download
added_tokens.json 35.0 B e17bde03 download

README current version from Hugging Face


license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: huihui-ai/gemma-3-4b-it-abliterated
tags:

  • abliterated
  • uncensored
  • mlx
  • mlx-my-repo

SnowFlash383935/gemma-3-4b-it-abliterated-mlx-6Bit

The Model SnowFlash383935/gemma-3-4b-it-abliterated-mlx-6Bit was converted to MLX format from huihui-ai/gemma-3-4b-it-abliterated using mlx-lm version 0.22.3.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("SnowFlash383935/gemma-3-4b-it-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. 2025-07-20Upload README.md with huggingface_hubec888181.4 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