Instructions to use CodeIsAbstract/HybridModelScratch_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeIsAbstract/HybridModelScratch_ with Transformers:
# Load model directly from transformers import HybridFourierLM model = HybridFourierLM.from_pretrained("CodeIsAbstract/HybridModelScratch_", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,051 Bytes
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library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: HybridModelScratch_
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HybridModelScratch_
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2920
- Accuracy: 0.2203
## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 150
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 32.0977 | 0.1 | 100 | 7.3025 | 0.1141 |
| 26.0238 | 0.2 | 200 | 6.3336 | 0.1618 |
| 24.2338 | 0.3 | 300 | 5.9560 | 0.1830 |
| 23.2729 | 0.4 | 400 | 5.7180 | 0.1967 |
| 22.5651 | 0.5 | 500 | 5.5544 | 0.2059 |
| 22.0473 | 0.6 | 600 | 5.4359 | 0.2119 |
| 21.7227 | 0.7 | 700 | 5.3620 | 0.2162 |
| 21.4768 | 0.8 | 800 | 5.3150 | 0.2189 |
| 21.3548 | 0.9 | 900 | 5.2958 | 0.2200 |
| 21.3675 | 1.0 | 1000 | 5.2920 | 0.2203 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu129
- Datasets 5.0.0
- Tokenizers 0.22.0
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