| | --- |
| | base_model: nilq/baby-python-mistral-1L-tiny-base |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - nilq/small-lua-stack |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: baby-python-mistral-1L-tiny-lua-ft |
| | results: |
| | - task: |
| | name: Causal Language Modeling |
| | type: text-generation |
| | dataset: |
| | name: nilq/small-lua-stack |
| | type: nilq/small-lua-stack |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.4940860736493237 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # baby-python-mistral-1L-tiny-lua-ft |
| |
|
| | This model is a fine-tuned version of [nilq/baby-python-mistral-1L-tiny-base](https://huggingface.co/nilq/baby-python-mistral-1L-tiny-base) on the nilq/small-lua-stack dataset. This is the Lua model in the paper [Tracking Universal Features Through Fine-Tuning and Model Merging](https://arxiv.org/abs/2410.12391). |
| | It achieves the following results on the evaluation set: |
| | - Loss: 2.4518 |
| | - Accuracy: 0.4941 |
| |
|
| | ## 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: 64 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_steps: 500 |
| | - num_epochs: 1.0 |
| | |
| | ### Training results |
| | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.38.1 |
| | - Pytorch 2.2.0+cu121 |
| | - Datasets 2.17.1 |
| | - Tokenizers 0.15.2 |
| | |
