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
Model save
Browse files- README.md +70 -0
- adapter_model.safetensors +1 -1
README.md
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---
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library_name: peft
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license: apache-2.0
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base_model: google/mt5-small
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tags:
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- base_model:adapter:google/mt5-small
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- lora
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- transformers
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model-index:
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- name: mt5-small-finetuned-medium
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# mt5-small-finetuned-medium
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.9152
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
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No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 0.03
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 23.6513 | 0.4578 | 500 | 4.2624 |
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| 22.3063 | 0.9155 | 1000 | 4.1012 |
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| 21.6209 | 1.3726 | 1500 | 4.0121 |
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| 21.1642 | 1.8304 | 2000 | 3.9515 |
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| 21.0332 | 2.2875 | 2500 | 3.9277 |
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| 20.8154 | 2.7453 | 3000 | 3.9152 |
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### Framework versions
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- PEFT 0.18.1
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- Transformers 5.2.0
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- Pytorch 2.9.0+cu126
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 1027183888
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version https://git-lfs.github.com/spec/v1
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size 1027183888
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