Huihui-Qwen3.6-35B-A3B-abliterated-mlx-bf16
MLX-VLM conversion of huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated.
Overview
- Format: MLX-VLM
- Precision: bf16
- Size: 66G
- Source model type: Qwen3_5MoeForConditionalGeneration
- Source pipeline: image-text-to-text
- Intended runtime: mlx-vlm, LM Studio
Validation
Local checks on Apple Silicon:
- model loading in mlx-vlm: passed
- text generation smoke test: passed
- image generation path smoke test: passed
Smoke-test logs were produced locally before upload.
Notes
This is an abliterated model conversion. Outputs may be less filtered than standard instruction-tuned models. Use responsibly and comply with applicable laws and platform policies.
Files
Important files in this repo:
- config.json
- generation_config.json
- chat_template.jinja
- tokenizer.json
- tokenizer_config.json
- model.safetensors.index.json
- model-*.safetensors
Usage
Text generation
mlx_vlm.generate \
--model /path/to/Huihui-Qwen3.6-35B-A3B-abliterated-mlx-bf16 \
--prompt "你好" \
--max-tokens 128 \
--trust-remote-code \
--processor-kwargs '{"enable_thinking": false}'
Image prompt
mlx_vlm.generate \
--model /path/to/Huihui-Qwen3.6-35B-A3B-abliterated-mlx-bf16 \
--image /path/to/example.png \
--prompt "Describe this image." \
--max-tokens 128 \
--trust-remote-code \
--processor-kwargs '{"enable_thinking": false}'
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Model size
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Tensor type
BF16
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Base model
Qwen/Qwen3.6-35B-A3B
# 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("vanch007/Huihui-Qwen3.6-35B-A3B-abliterated-mlx-bf16") config = load_config("vanch007/Huihui-Qwen3.6-35B-A3B-abliterated-mlx-bf16") # 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)