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--- |
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base_model: nilq/baby-python-mistral-1L-tiny-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- nilq/small-lua-stack |
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metrics: |
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- accuracy |
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model-index: |
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- name: baby-python-mistral-1L-tiny-lua-ft |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: nilq/small-lua-stack |
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type: nilq/small-lua-stack |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.4940860736493237 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# baby-python-mistral-1L-tiny-lua-ft |
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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). |
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It achieves the following results on the evaluation set: |
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- Loss: 2.4518 |
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- Accuracy: 0.4941 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 1.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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