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LethalDonkey/Gliese-Qwen3.5-9B-Abliterated-Caption-MLX-4bit

LethalDonkey Qwen 9.0B multimodal second-order
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Response includes
  • classification m1
  • files 11
  • hub_downloads_all_time 388
  • author_summary 21 models
  • readme_text full
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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
388
92 last 30d - stable
Likes
0
Descendants
1
in 1 direct fork
Model age
3mo ago
created 2026-06-17
Downloads over time
Now425→from45↑844%
2617231746345 on Jun 17425 on Oct 11JunJulAugSepOct
Jun 17 → Oct 11 · 56 snapshots · spans 116 days

Genealogy 1 direct fork

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
apache-2.0
Languages
en
Tags
mlx safetensors qwen3_5 apple-silicon qwen3_vl vision vision-language image-text-to-text abliterated uncensored 4-bit image-captioning

Related

Total size
5.54 GB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-06-17 01:25

Files by quantization

Auxiliary files 11 files 5.56 GB
model-00001-of-00002.safetensors 4.98 GB bb81e894 download
model-00002-of-00002.safetensors 573 MB b0a770bf download
tokenizer.json 19.1 MB 87a7830d download
model.safetensors.index.json 121 KB b1846cf0 download
chat_template.jinja 12.0 KB 3a5c99d4 download
config.json 3.64 KB 7e82059a download
README.md 2.84 KB 680c5809 download
.gitattributes 1.53 KB 52373fe2 download
tokenizer_config.json 1.14 KB 5344df8c download
processor_config.json 991 B 162cd9a2 download
generation_config.json 120 B 19800364 download

README current version from Hugging Face


license: apache-2.0
base_model: prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption
base_model_relation: quantized
library_name: mlx
pipeline_tag: image-text-to-text
language:

  • en
    tags:
  • mlx
  • apple-silicon
  • qwen3_vl
  • vision
  • vision-language
  • image-text-to-text
  • abliterated
  • uncensored
  • 4-bit
  • image-captioning
  • caption

Gliese-Qwen3.5-9B-Abliterated-Caption — MLX 4-bit

This is an MLX (4-bit) conversion of
prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption, a captioning-specialized, abliterated (refusal-removed) fine-tune of Qwen3-VL-8B-Instruct.

It runs natively on Apple Silicon (M-series) via Apple's
MLX framework and
mlx-vlm — typically faster than
llama.cpp/Metal for image encoding, with no separate vision-encoder (mmproj)
file needed.

As of conversion, no MLX build of this model existed — this is a
community conversion to bring it to Apple Silicon users.

Use it in an app

This model is wired into the
Qwen3-VL Captioner
desktop app — pick it from the MLX section of the model dropdown on a Mac and
it downloads + loads automatically.

Use it directly (mlx-vlm)

pip install mlx-vlm
from mlx_vlm import load, stream_generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

model, processor = load("LethalDonkey/Gliese-Qwen3.5-9B-Abliterated-Caption-MLX-4bit")
config = load_config("LethalDonkey/Gliese-Qwen3.5-9B-Abliterated-Caption-MLX-4bit")

messages = [
    {"role": "system", "content": "You are a helpful assistant that describes images accurately and in detail."},
    {"role": "user", "content": "Describe this image in detail."},
]
prompt = apply_chat_template(processor, config, messages, num_images=1)

for chunk in stream_generate(model, processor, prompt, image=["your_image.jpg"], max_tokens=512):
    print(chunk.text, end="", flush=True)

Quantization

  • Bits: 4
  • Format: MLX (safetensors), converted with mlx_vlm.convert
  • Choose 4-bit for the smallest size / lowest memory, 8-bit for the best
    quality, 6-bit for a balance.

Credits

License

Apache-2.0, inherited from the base model.

README history 1 version

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

  1. 2026-06-17MLX 4-bit conversion of prithivMLmods/Gliese-Qwen3.5-9B-Abliterated-Caption8f07e7b2.8 KB
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