Instructions to use uripper/HESS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uripper/HESS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="uripper/HESS")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("uripper/HESS") model = AutoModelForMaskedLM.from_pretrained("uripper/HESS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 866 Bytes
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"decompose_grad_sum": false,
"device_iterations": 5,
"embedding_serialization_factor": 2,
"enable_half_first_order_momentum": true,
"enable_half_partials": true,
"executable_cache_dir": "/tmp/exe_cache/",
"execute_encoder_on_cpu_for_generation": false,
"gradient_accumulation_steps": 64,
"inference_device_iterations": 4,
"inference_replication_factor": 4,
"ipus_per_replica": 4,
"layers_per_ipu": [
3,
7,
7,
7
],
"matmul_proportion": [
0.15,
0.18,
0.2,
0.25
],
"optimizer_state_offchip": true,
"optimum_version": "1.4.1",
"output_mode": "final",
"profile_dir": "",
"recompute_checkpoint_every_layer": true,
"replicated_tensor_sharding": true,
"replication_factor": 4,
"seed": 42,
"sharded_execution_for_inference": false,
"transformers_version": "4.20.0",
"use_popdist": false
}
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