---
dataset_info:
  features:
  - name: depth
    dtype: int64
  - name: width
    dtype: int64
  - name: tokens
    dtype: int64
  - name: FLOPs_per_token
    dtype: float64
  - name: FLOPs
    dtype: float64
  - name: params
    dtype: float64
  - name: params_with_embeds
    dtype: float64
  - name: FLOPs_6N
    dtype: float64
  - name: params_pred_loss
    dtype: float64
  - name: wd_ratio
    dtype: float64
  - name: wd_pred_loss
    dtype: float64
  - name: bucket
    dtype: string
  splits:
  - name: train
    num_bytes: 1772
    num_examples: 13
  download_size: 6825
  dataset_size: 1772
configs:
- config_name: default
  data_files:
  - split: train
    path: mins_1e-3/mins_lr_ablation_hot_width_depth_params_relaxed_params/train-*
license: mit
---
This dataset is my cache for the [scaling-laws](https://github.com/mcleish7/gemstone-scaling-laws) related to the [gemstone models](https://huggingface.co/collections/tomg-group-umd/gemstone-models-679408ee3f19f1d4d00e8b10).

In `data_cache` is the approach 3 data cache with the mins for `delta=1e-4`, the mins for `delta=1e-3` are in `mins_1e-3`.

This is the code I used to upload it:
```
import pandas as pd
from datasets import Dataset
import os
import gc


def get_data_dict(path):
    contents = os.listdir(path)

    ds_store = {}
    for i, file in enumerate(contents):
        gc.collect()
        df = pd.read_parquet(f"{path}{file}")
        for col in df.columns:
            if pd.api.types.is_interval_dtype(df[col]):
                df[col] = df[col].astype(str)

        hf_dataset = Dataset.from_pandas(df)
        ds_store[file.replace(".parquet", "")] = hf_dataset
        hf_dataset.push_to_hub(
            "smcleish/scaling-laws-cache",
            private=True,
            data_dir=path.split("/")[1] + "/" + file.replace(".parquet", ""),
        )
        gc.collect()


ds_1 = get_data_dict("plotters/data_cache/")
ds_2 = get_data_dict("plotters/mins_1e-3/")
```
To download it do the oppostite of this. The cache is very large, so maybe target specific files you would like. The approach 3 code is expecting pandas `.parquet` files.
Please open a discussion with any questions as this is currently very experimental.