Instructions to use jla25/square1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jla25/square1.2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jla25/square1.2") model = AutoModelForSeq2SeqLM.from_pretrained("jla25/square1.2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from jla25/square1.2: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/jla25/square1.2/resolve/main/README.md
- Command line
-
hf download hf://jla25/square1.2/README.md
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curl -L -o README.md https://huggingface.co/jla25/square1.2/resolve/main/README.md
1.76 kB
metadata
library_name: transformers
license: mit
base_model: jla25/results
tags:
- generated_from_trainer
model-index:
- name: square1.2
results: []
square1.2
This model is a fine-tuned version of jla25/results on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0074
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 75 | 0.0286 |
| No log | 2.0 | 150 | 0.0172 |
| No log | 3.0 | 225 | 0.0129 |
| No log | 4.0 | 300 | 0.0103 |
| No log | 5.0 | 375 | 0.0088 |
| No log | 6.0 | 450 | 0.0082 |
| 0.0187 | 7.0 | 525 | 0.0078 |
| 0.0187 | 8.0 | 600 | 0.0076 |
| 0.0187 | 9.0 | 675 | 0.0074 |
| 0.0187 | 10.0 | 750 | 0.0074 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Tokenizers 0.19.1