license: apache-2.0
base_model: prithivMLmods/Gliese-Qwen3.5-4B-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-Qwen3.5-4B-Abliterated-Caption — MLX 4-bit
This is an MLX (4-bit) conversion ofprithivMLmods/Gliese-Qwen3.5-4B-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 andmlx-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-Qwen3.5-4B-Abliterated-Caption-MLX-4bit")
config = load_config("LethalDonkey/Gliese-Qwen3.5-4B-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
- Base model: prithivMLmods/Gliese-Qwen3.5-4B-Abliterated-Caption by prithivMLmods
- Original architecture: Qwen3-VL by Qwen / Alibaba
- MLX framework: Apple (ml-explore/mlx)
- VLM tooling: mlx-vlm (Blaizzy/mlx-vlm)
- Converted for the Qwen3-VL Captioner project
License
Apache-2.0, inherited from the base model.