Instructions to use PQlet/T5base-lora-sumarizationTables-v2-MLM-lambda0.01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use PQlet/T5base-lora-sumarizationTables-v2-MLM-lambda0.01 with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") model = PeftModel.from_pretrained(base_model, "PQlet/T5base-lora-sumarizationTables-v2-MLM-lambda0.01") - Notebooks
- Google Colab
- Kaggle
File size: 652 Bytes
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"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": "t5-base",
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 32,
"lora_dropout": 0.1,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"r": 8,
"rank_pattern": {},
"revision": null,
"target_modules": [
"v",
"q"
],
"task_type": "SEQ_2_SEQ_LM",
"use_dora": false,
"use_rslora": false
} |