Instructions to use CodeIsAbstract/HybridTimeScaleModel_conti3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeIsAbstract/HybridTimeScaleModel_conti3 with Transformers:
# Load model directly from transformers import HybridTimeScaleLM model = HybridTimeScaleLM.from_pretrained("CodeIsAbstract/HybridTimeScaleModel_conti3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,554 Bytes
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library_name: transformers
base_model: CodeIsAbstract/HybridTimeScaleModel_conti2
tags:
- generated_from_trainer
model-index:
- name: HybridTimeScaleModel_conti3
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. -->
# HybridTimeScaleModel_conti3
This model is a fine-tuned version of [CodeIsAbstract/HybridTimeScaleModel_conti2](https://huggingface.co/CodeIsAbstract/HybridTimeScaleModel_conti2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2616
## 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-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- 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: constant_with_warmup
- lr_scheduler_warmup_steps: 10
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1777 | 0.05 | 50 | 3.4269 |
| 3.1601 | 0.1 | 100 | 3.3961 |
| 3.1383 | 0.15 | 150 | 3.3838 |
| 3.0848 | 0.2 | 200 | 3.3661 |
| 3.0850 | 0.25 | 250 | 3.3512 |
| 3.0674 | 0.3 | 300 | 3.3399 |
| 3.0248 | 0.35 | 350 | 3.3282 |
| 3.0237 | 0.4 | 400 | 3.3229 |
| 3.0460 | 0.45 | 450 | 3.3178 |
| 3.0554 | 0.5 | 500 | 3.3105 |
| 3.0215 | 0.55 | 550 | 3.3045 |
| 3.0404 | 0.6 | 600 | 3.2980 |
| 3.0302 | 0.65 | 650 | 3.2929 |
| 3.0227 | 0.7 | 700 | 3.2861 |
| 2.9928 | 0.75 | 750 | 3.2805 |
| 3.0219 | 0.8 | 800 | 3.2757 |
| 2.9899 | 0.85 | 850 | 3.2712 |
| 2.9925 | 0.9 | 900 | 3.2690 |
| 2.9400 | 0.95 | 950 | 3.2654 |
| 2.9780 | 1.0 | 1000 | 3.2616 |
### Framework versions
- Transformers 5.14.1
- Pytorch 2.8.0+cu128
- Datasets 5.0.1
- Tokenizers 0.22.2
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