Update README.md

README.md

@@ -36,15 +36,30 @@ Get access now at [LLM360 site](https://www.llm360.ai/)
 
 # Loading Amber's Pretraining Data
 
+Below is an example of how to download, sample, and detokenize any subset of AmberDatasets corresponding to an Amber checkpoint. Just set the `CHECKPOINT_NUM` to the subset you are interested in (0-359) and point `CHECKPOINT_PATH` to the local checkpoint folder. 
+
 ```python
-import datasets
+import random
+from transformers import AutoTokenizer
+from datasets import load_dataset
+
+CHECKPOINT_NUM = 0  # Pretraining dataset for checkpoint
+NUM_SAMPLES = 10  # Number of random samples to decode
+CHECKPOINT_PATH = "/path/to/ckpt_000/"  # Local path to a Amber checkpoint
+
+dataset = load_dataset(
+    "LLM360/AmberDatasets",
+    data_files=f"train/train_{CHECKPOINT_NUM:03}.jsonl",
+    split=None,
+)
 
-dataset = datasets.load_dataset('llm360/amber', chunk_idx=111)
+tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH)
+samples = set(random.choices(range(len(dataset["train"])), k=NUM_SAMPLES))
 
-print(dataset[0])
-print(len(dataset[0]))
-# [1, 5, 9, 2, 6, ...]
-# 2049
+for i, line in enumerate(dataset["train"]):
+    if i in samples:
+        tokens = line["token_ids"]
+        print(f"{i}:{tokenizer.decode(tokens)}")
 ```