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TxemAI/gemma-4-31B-uncensored-heretic-mlx-4bit

TxemAI Gemma 31B second-order
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Response includes
  • classification m3
  • files 30
  • benchmarks 11 entries
  • hub_downloads_all_time 1,622
  • author_summary 2 models
  • readme_text full
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Abliteration classifier · v1.0.0
M3
Primary method

Layer-wise ablation

Applied on top of direct removal inherited from the base model.
Confidence
HIGH
Inherited from base model
Why this label 2 signals
Producer identity confirmed by naming conventions, tags or the model card. This label is very unlikely to change.
  • 'heretic' in model name (Heretic-produced)
  • Heretic uses layer-wise optimization (M3) with underlying direction removal (M1)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
HIGH
Why we say so
name contains 'heretic'; Heretic default extraction is difference-of-means (Arditi 2024)
Downloads · lifetime
2K
46 last 30d - cooling
Likes
0
Model age
6mo ago
created 2026-04-06
Downloads over time
Now1.6K→from1.1K↑55%
1K1.2K1.5K1.7K1.1K on Apr 151.6K on Oct 111.6K on Oct 10AprMayJunJulAugSepOct
Apr 15 → Oct 11 · 65 snapshots · spans 179 days

Benchmarks

Benchmark Score Source
Entertainment 2.7 UGI
Hazardous 4.7 UGI
Natural Intelligence 34.73 UGI
Political lean -18.5% UGI
Sensitive-Info 33.23 UGI
SocPol 3 UGI
UGI 53.82 UGI
Willingness (10) 9.5 UGI
W10-Adherence 10 UGI
W10-Direct 9 UGI
Writing 38.26 UGI

Genealogy 0 direct forks

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This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

License
apache-2.0
Tags
mlx-vlm safetensors gemma4 mlx 4-bit apple-silicon base_model:llmfan46/gemma-4-31B-it-uncensored-heretic base_model:quantized:llmfan46/gemma-4-31B-it-uncensored-heretic license:apache-2.0 region:us

Related

Total size
26.8 GB
Files
30
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-04-06 06:25

Files by quantization

Auxiliary files 30 files 26.9 GB
model-00004-of-00006.safetensors 5.00 GB a37a4480 download
model-00003-of-00006.safetensors 5.00 GB f69446cb download
model-00005-of-00006.safetensors 4.98 GB 0213aa37 download
model-00002-of-00006.safetensors 4.98 GB be71bb55 download
model-00001-of-00006.safetensors 4.96 GB 33245b07 download
model-00006-of-00006.safetensors 1.92 GB fb800784 download
._model-00001-of-00006.safetensors 4.00 KB 0be73c8c download
._model-00002-of-00006.safetensors 4.00 KB 0be73c8c download
._model-00003-of-00006.safetensors 4.00 KB 0be73c8c download
._model-00004-of-00006.safetensors 4.00 KB 0be73c8c download
._model-00005-of-00006.safetensors 4.00 KB 0be73c8c download
._model-00006-of-00006.safetensors 4.00 KB 0be73c8c download
tokenizer.json 30.7 MB a2619fe1 download
model.safetensors.index.json 201 KB 171dafee download
config.json 47.8 KB 98db386c download
chat_template.jinja 11.8 KB 33c51c2d download
._.cache 4.00 KB dc7b9deb download
._README.md 4.00 KB dc7b9deb download
._chat_template.jinja 4.00 KB dc7b9deb download
._config.json 4.00 KB dc7b9deb download
._generation_config.json 4.00 KB dc7b9deb download
._model.safetensors.index.json 4.00 KB dc7b9deb download
._processor_config.json 4.00 KB dc7b9deb download
._tokenizer.json 4.00 KB dc7b9deb download
._tokenizer_config.json 4.00 KB dc7b9deb download
tokenizer_config.json 2.70 KB 8abee96e download
README.md 2.66 KB 84c9b7be download
.gitattributes 1.53 KB 52373fe2 download
processor_config.json 902 B 13e92a44 download
generation_config.json 217 B ed42ae71 download

README current version from Hugging Face


license: apache-2.0
tags:

  • mlx
  • gemma4
  • 4-bit
  • apple-silicon
    library_name: mlx-vlm
    base_model: llmfan46/gemma-4-31B-it-uncensored-heretic

gemma-4-31B-uncensored-heretic · MLX 4-bit

MLX conversion of llmfan46/gemma-4-31B-it-uncensored-heretic, a fine-tune of Google's Gemma 4 31B Instruct. Quantized to ~7.4 bits per weight using mlx-vlm v0.4.3 on Apple Silicon.

If you have enough RAM, the Q8 version offers near-lossless quality.

Performance on Apple M4 Max · 128 GB

  • Peak memory: ~29 GB
  • Prompt throughput: ~39.9 tok/s
  • Generation speed: ~16.9 tok/s

Requirements

pip install -U mlx-vlm

Gemma 4 support requires mlx-vlm >= 0.4.3. Standard mlx-lm does not yet support the gemma4 architecture.

Usage

Text only

python -m mlx_vlm generate \
  --model TxemAI/gemma-4-31B-uncensored-heretic-mlx-4bit \
  --prompt "Your prompt here" \
  --max-tokens 512

With image

python -m mlx_vlm generate \
  --model TxemAI/gemma-4-31B-uncensored-heretic-mlx-4bit \
  --prompt "Describe this image." \
  --image path/to/image.jpg \
  --max-tokens 512

Python API

from mlx_vlm import load, generate

model, processor = load("TxemAI/gemma-4-31B-uncensored-heretic-mlx-4bit")

response = generate(
    model,
    processor,
    prompt="Your prompt here",
    max_tokens=512,
    temperature=0.7,
)
print(response)

Which version should I use?

Precision Peak RAM Gen speed Quality
BF16 (full) ~62 GB slowest reference
Q8 ~34 GB ~14.5 tok/s near-lossless
Q4 (this model) ~29 GB ~16.9 tok/s good

Q4 is the recommended version for machines with 32 GB unified memory (M2/M3 Pro, M1 Max, M3 Max).

Notes

  • The model activates Gemma 4's thinking channel (<|channel>thought) on reasoning-heavy prompts — this is expected behaviour.
  • The mel filter warning on load is harmless; it relates to the audio encoder and does not affect text or vision inference.
  • Unofficial community conversion. For the original fine-tune see llmfan46/gemma-4-31B-it-uncensored-heretic.

Conversion

python -m mlx_vlm convert \
  --hf-path llmfan46/gemma-4-31B-it-uncensored-heretic \
  --mlx-path ./gemma-4-31B-uncensored-heretic-mlx-4bit \
  --quantize --q-bits 4

Credits

  • Google DeepMind — Gemma 4 base model
  • llmfan46 — uncensored-heretic fine-tune
  • ml-explore — MLX framework
  • Blaizzy — mlx-vlm library

README history 2 versions

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

  1. 2026-04-06Update README.md92bf2ad2.7 KB
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  2. 2026-04-06Upload folder using huggingface_hub46270a984 B
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