---
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