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---
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
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