Instructions to use stefanj0/t5gemma-math-corrector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use stefanj0/t5gemma-math-corrector with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5gemma-s-s-ul2-it") model = PeftModel.from_pretrained(base_model, "stefanj0/t5gemma-math-corrector") - Transformers
How to use stefanj0/t5gemma-math-corrector with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("stefanj0/t5gemma-math-corrector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 200, checkpoint
Browse files- last-checkpoint/adapter_config.json +6 -6
- last-checkpoint/adapter_model.safetensors +2 -2
- last-checkpoint/optimizer.pt +2 -2
- last-checkpoint/rng_state.pth +1 -1
- last-checkpoint/scaler.pt +1 -1
- last-checkpoint/scheduler.pt +1 -1
- last-checkpoint/trainer_state.json +29 -137
- last-checkpoint/training_args.bin +1 -1
last-checkpoint/adapter_config.json
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|
| 59 |
},
|
| 60 |
"attributes": {
|
| 61 |
+
"early_stopping_patience_counter": 0
|
| 62 |
}
|
| 63 |
},
|
| 64 |
"TrainerControl": {
|
|
|
|
| 67 |
"should_evaluate": false,
|
| 68 |
"should_log": false,
|
| 69 |
"should_save": true,
|
| 70 |
+
"should_training_stop": false
|
| 71 |
},
|
| 72 |
"attributes": {}
|
| 73 |
}
|
| 74 |
},
|
| 75 |
+
"total_flos": 763083148492800.0,
|
| 76 |
"train_batch_size": 4,
|
| 77 |
"trial_name": null,
|
| 78 |
"trial_params": null
|
last-checkpoint/training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 5905
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8c67ac096c206b245d26f775afa3fb8ffa810038da5c0f227d1b18811d5d602b
|
| 3 |
size 5905
|