65 lines
1.5 KiB
Markdown
65 lines
1.5 KiB
Markdown
---
|
|
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
|