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cotysong113_-_my_awesome_eli5_clm-model-gguf
RichardErkhov/cotysong113_-_my_awesome_eli5_clm-model-gguf
cotysong113_-_my_awesome_eli5_clm-model-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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At a glance
- Access
- Public
- Created
- Feb 11, 2025
- Updated
- Feb 12, 2025
- SHA
- 0bd15c09
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
my_awesome_eli5_clm-model - GGUF
- Model creator: https://huggingface.co/cotysong113/
- Original model: https://huggingface.co/cotysong113/my_awesome_eli5_clm-model/
Original model description:
library_name: transformers license: apache-2.0 base_model: distilbert/distilgpt2 tags:
- generated_from_trainer datasets:
- eli5_category model-index:
- name: my_awesome_eli5_clm-model 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. -->
my_awesome_eli5_clm-model
This model is a fine-tuned version of distilbert/distilgpt2 on the eli5_category dataset. It achieves the following results on the evaluation set:
- Loss: 3.7168
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 274 | 3.7314 |
| 3.8123 | 2.0 | 548 | 3.7202 |
| 3.8123 | 3.0 | 822 | 3.7168 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.21.0
- Created
- Feb 11, 2025
- Updated
- Feb 12, 2025