Instructions to use kallacharanteja/mt5-small-finetuned-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kallacharanteja/mt5-small-finetuned-medium with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small") model = PeftModel.from_pretrained(base_model, "kallacharanteja/mt5-small-finetuned-medium") - Transformers
How to use kallacharanteja/mt5-small-finetuned-medium with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kallacharanteja/mt5-small-finetuned-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 3279
Browse files- adapter_model.safetensors +1 -1
- trainer_state.json +39 -4
adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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trainer_state.json
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"best_global_step": 3000,
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"best_metric": 3.9151711463928223,
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"best_model_checkpoint": "/kaggle/working/checkpoints/checkpoint-3000",
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"epoch":
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"eval_samples_per_second": 59.195,
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"step": 3000
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"epoch": 3.0,
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"eval_steps": 500,
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"global_step": 3279,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"eval_samples_per_second": 59.195,
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"eval_steps_per_second": 7.462,
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"step": 3000
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