Instructions to use AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX", dtype="auto")

MLX

How to use AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX")
config = load_config("AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX

SGLang

How to use AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX with Docker Model Runner:
```
docker model run hf.co/AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX
```

Huihui-Qwen3.5-2B-abliterated — fp16 MLX

MLX-converted version of huihui-ai/Huihui-Qwen3.5-2B-abliterated for Apple Silicon.

Model Details

Property	Value
Base model	Qwen3.5-2B
Type	Vision-Language Model (VLM)
Format	MLX fp16 (bfloat16)
Size	~4.5 GB
Abliterated	Yes — censorship layers removed

Variants

Variant	Size	Quality	Link
fp16	~4.5 GB	Highest	This repo
MXFP8	~2.6 GB	Near-native	mxfp8
MXFP4	~1.7 GB	Good	mxfp4

Usage

pip install mlx-vlm

# Text generation
python -m mlx_vlm.generate \
  --model AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX \
  --prompt "Describe this image in detail" \
  --image <path-or-url>

# Chat UI
python -m mlx_vlm.chat_ui \
  --model AITRADER/Huihui-Qwen3.5-2B-abliterated-fp16-MLX

Credits

Original model: huihui-ai
Base architecture: Qwen
MLX conversion: AITRADER

Downloads last month: 68

Safetensors

Model size

2B params

Tensor type

BF16

F32

MLX

Hardware compatibility

Quantized

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