---
tags:
- generated_from_trainer
datasets:
- roneneldan/TinyStories
metrics:
- accuracy
model-index:
- name: mistral-1L-tiny
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: roneneldan/TinyStories
      type: roneneldan/TinyStories
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5792084706530948
---

# mistral-1L-tiny

A tiny single-layer 35.1M parameter Mistral model, with a hidden size of 512, and an MLP intermediate size of 1024.
This model is trained on the roneneldan/TinyStories dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6868
- Accuracy: 0.5792

## Model description

This work is inspired by the 21M parameter one-layer GPT-Neo of the [Tiny Stories paper](https://arxiv.org/abs/2305.07759).
Results reproduced to acquire high-frequency checkpoints for further analysis.

## Intended uses & limitations

Analysis of feature dynamics and emergence in real-world language models.

## Training procedure

Trained for 90171 steps, corresponding to ~2 hours on a single H100.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0006
- 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
- num_epochs: 3.0

### Training results

Quite consistent English text generation.

### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2