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README.md

@@ -1,17 +1,71 @@
 ---
-dataset_info:
-  features:
-  - name: tokens
-    list: int64
-  splits:
-  - name: train
-    num_bytes: 6532121844
-    num_examples: 796989
-  download_size: 1451384768
-  dataset_size: 6532121844
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: mit
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- tokenized
+- language-modeling
+size_categories:
+- 100K<n<1M
 ---
+# Dataset Card for eoinf/pile_llama_800m
+
+## Original Dataset
+Original dataset: monology/pile-uncopyrighted
+
+## Dataset Details
+
+- **Total Tokens**: 816,116,736
+- **Total Sequences**: 796,989
+- **Context Length**: 1024 tokens
+- **Tokenizer**: meta-llama/Llama-2-7b-hf
+- **Format**: Each example contains a single field `tokens` with a list of 1024 token IDs
+
+## Preprocessing
+
+Each document was:
+1. Tokenized using the meta-llama/Llama-2-7b-hf tokenizer
+2. Prefixed with a BOS (beginning of sequence) token
+3. Suffixed with an EOS (end of sequence) token
+4. Packed into fixed-length sequences of 1024 tokens
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("eoinf/pile_llama_800m")
+
+# Access training data
+train_data = dataset["train"]
+print(train_data[0]["tokens"])  # First sequence
+```
+
+## Use with PyTorch
+
+```python
+import torch
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+
+dataset = load_dataset("eoinf/pile_llama_800m", split="train")
+
+# Convert to PyTorch tensors
+dataset.set_format(type="torch", columns=["tokens"])
+
+# Create DataLoader
+dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
+
+for batch in dataloader:
+    tokens = batch["tokens"]  # Shape: (batch_size, 1024)
+    # Your training code here
+```
+
+## Dataset Statistics
+
+- Total storage size: ~1.63 GB (as uint16)
+- Sequences per batch folder: 10,000
+- Number of batch folders: 80