Visual Question Answering
Transformers
Safetensors
English
Chinese
designer-instruct
image-text-to-text
vision
multimodal
Mixture of Experts
design
zen
zenlm
hanzo
Instructions to use zenlm/zen-designer-235b-a22b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenlm/zen-designer-235b-a22b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="zenlm/zen-designer-235b-a22b-instruct")# Load model directly from transformers import AutoModelForMultimodalLM model = AutoModelForMultimodalLM.from_pretrained("zenlm/zen-designer-235b-a22b-instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 771 Bytes
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"architectures": [
"Qwen3VLForConditionalGeneration"
],
"model_type": "designer-instruct",
"torch_dtype": "bfloat16",
"transformers_version": "4.44.2",
"thinking_tokens": 512000,
"_name_or_path": "zenlm/zen-designer-235b-a22b-instruct",
"_base_model": "zenlm/zen-designer-235b-a22b-instruct",
"vision_config": {
"hidden_size": 2048,
"image_size": 2048,
"num_hidden_layers": 48,
"patch_size": 14
},
"text_config": {
"vocab_size": 151936,
"hidden_size": 8192,
"num_hidden_layers": 80,
"num_attention_heads": 64,
"num_key_value_heads": 8,
"max_position_embeddings": 131072
},
"num_experts": 64,
"num_experts_per_tok": 4,
"expert_interval": 1,
"_total_params": "235B",
"_active_params": "22B"
}
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