Instructions to use ApacheOne/expimodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ApacheOne/expimodel with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ApacheOne/expimodel", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,718 Bytes
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"architectures": [
"Qwen3VLForConditionalGeneration"
],
"dtype": "bfloat16",
"image_token_id": 151655,
"model_type": "qwen3_vl",
"text_config": {
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 12288,
"max_position_embeddings": 262144,
"model_type": "qwen3_vl_text",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"mrope_interleaved": true,
"mrope_section": [
24,
20,
20
],
"rope_type": "default"
},
"rope_theta": 5000000,
"use_cache": true,
"vocab_size": 151936
},
"tie_word_embeddings": false,
"transformers_version": "4.57.6",
"video_token_id": 151656,
"vision_config": {
"deepstack_visual_indexes": [
8,
16,
24
],
"depth": 27,
"dtype": "bfloat16",
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"in_channels": 3,
"initializer_range": 0.02,
"intermediate_size": 4304,
"model_type": "qwen3_vl",
"num_heads": 16,
"num_position_embeddings": 2304,
"out_hidden_size": 4096,
"patch_size": 16,
"spatial_merge_size": 2,
"temporal_patch_size": 2
},
"vision_end_token_id": 151653,
"vision_start_token_id": 151652,
"quantization_config": {
"config_groups": {
"group_0": {
"input_activations": {
"dynamic": false,
"num_bits": 4,
"type": "float",
"group_size": 16
},
"weights": {
"dynamic": false,
"num_bits": 4,
"type": "float",
"group_size": 16
},
"targets": [
"Linear"
]
}
},
"ignore": [
"lm_head",
"model.visual.blocks.0.mlp*",
"model.visual.blocks.1.mlp*",
"model.visual.blocks.10.mlp*",
"model.visual.blocks.11.mlp*",
"model.visual.blocks.12.mlp*",
"model.visual.blocks.13.mlp*",
"model.visual.blocks.14.mlp*",
"model.visual.blocks.15.mlp*",
"model.visual.blocks.16.mlp*",
"model.visual.blocks.17.mlp*",
"model.visual.blocks.18.mlp*",
"model.visual.blocks.19.mlp*",
"model.visual.blocks.2.mlp*",
"model.visual.blocks.20.mlp*",
"model.visual.blocks.21.mlp*",
"model.visual.blocks.22.mlp*",
"model.visual.blocks.23.mlp*",
"model.visual.blocks.24.mlp*",
"model.visual.blocks.25.mlp*",
"model.visual.blocks.26.mlp*",
"model.visual.blocks.3.mlp*",
"model.visual.blocks.4.mlp*",
"model.visual.blocks.5.mlp*",
"model.visual.blocks.6.mlp*",
"model.visual.blocks.7.mlp*",
"model.visual.blocks.8.mlp*",
"model.visual.blocks.9.mlp*"
],
"quant_algo": "NVFP4",
"producer": {
"name": "modelopt",
"version": "0.43.0"
},
"quant_method": "modelopt"
}
} |