Instructions to use miguelcsx/prism-control with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miguelcsx/prism-control with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="miguelcsx/prism-control", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("miguelcsx/prism-control", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,274 Bytes
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"model_type": "tolm",
"architectures": [
"TOLMForMaskedLM"
],
"auto_map": {
"AutoConfig": "tolm.TOLMConfig",
"AutoModel": "tolm.TOLMModel",
"AutoModelForMaskedLM": "tolm.TOLMForMaskedLM",
"AutoModelForCausalLM": "tolm.TOLMForCausalLM"
},
"vocab_size": 16384,
"max_seq_len": 512,
"hidden_size": 384,
"num_hidden_layers": 12,
"num_attention_heads": 6,
"intermediate_size": 1280,
"position_buckets": 32,
"dropout": 0.1,
"attention_dropout": 0.1,
"initializer_range": 0.03227486121839514,
"value_gating": true,
"residual_mixing": true,
"pad_token_id": 3,
"bos_token_id": 1,
"eos_token_id": 2,
"mask_token_id": 4,
"absolute_positions": false,
"use_rope": false,
"use_alibi": false,
"recurrent_steps": 1,
"num_experts": 1,
"experts_per_token": 1,
"expert_intermediate_size": null,
"future_offsets": [],
"state_mixer_kernel": 0,
"rtd_auxiliary": true,
"geometry_lexical_dim": 0,
"geometry_curvature": 1.0,
"cognitive_readout_layer": 0,
"cognitive_readout_weight": 0.0,
"direct_sum_dims": [],
"direct_sum_heads": [],
"direct_sum_intermediate_sizes": [],
"lexical_residual_buckets": 0,
"lexical_residual_dim": 0,
"lexical_residual_scale": 1.0,
"structured_projection_dim": 0
}
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