Instructions to use NeuroTechX/zuna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NeuroTechX/zuna with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="NeuroTechX/zuna", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NeuroTechX/zuna", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,434 Bytes
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"adaptive_loss_weighting": true,
"architectures": [
"ZunaModel"
],
"bottleneck_type": "mmd",
"codebook_size": 1024,
"compression_free_conv_stem": false,
"decoder_encoder_dropout": 0.1,
"decoder_repa_index": {
"__float__": "Infinity"
},
"decoder_timestep_dropout": 0.1,
"dim": 1024,
"distill_output_dim": 0,
"dont_noise_chan_xyz": false,
"dropout_type": "zeros",
"dtype": "float32",
"encoder_hidden_dim": null,
"encoder_input_dim": 32,
"encoder_latent_downsample_factor": 1,
"encoder_output_dim": 32,
"encoder_repa_index": {
"__float__": "Infinity"
},
"encoder_sliding_window": 65536,
"ffn_dim_multiplier": null,
"head_dim": 64,
"huber_c": null,
"init_base_std": 0.02,
"init_std_factor": "disabled",
"input_dim": 32,
"learnable_bias": false,
"levels": [],
"max_seqlen": 50,
"model_type": "zuna",
"multiple_of": 256,
"n_heads": 8,
"n_kv_heads": null,
"n_layers": 16,
"norm_eps": 1e-05,
"num_fine_time_pts": 32,
"repa_dim": 1024,
"repa_loss_fn": "cosine",
"rope_dim": 4,
"rope_theta": 10000.0,
"seed": 42,
"seqlen_t": false,
"sliding_window": 65536,
"stft_global_sigma": 0.1,
"t_dim": 64,
"tok_idx_type": "{x,y,z,tc}",
"transformers_version": "5.2.0",
"weight_tying": false,
"xattn_sliding_window": 65536,
"auto_map": {
"AutoModel": "modeling_zuna.ZunaModel",
"AutoConfig": "configuration_zuna.ZunaConfig"
}
} |