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. 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