diff --git a/gpt4all/index/embed_texts.py b/gpt4all/index/embed_texts.py
new file mode 100644
index 00000000..f130217d
--- /dev/null
+++ b/gpt4all/index/embed_texts.py
@@ -0,0 +1,96 @@
+import torch.distributed as dist
+from argparse import ArgumentParser
+from datasets import Dataset
+from gpt4all.index.embed import Embedder
+from gpt4all.utils.distributed_utils import rank0_print
+from torch.utils.data import DataLoader, DistributedSampler
+from transformers.trainer_pt_utils import nested_numpify
+from transformers import BatchEncoding
+from tqdm import tqdm
+import numpy as np
+import torch
+
+
+class PadCollateInputs:
+    def __init__(self, tokenizer):
+        self.tokenizer = tokenizer
+
+    def __call__(self, batch):
+        mapped_inputs = {"input_ids": [], "attention_mask": []}
+        mapped_inputs["input_ids"] = [b["tokenized_chunk"] for b in batch]
+        mapped_inputs["attention_mask"] = [b["tokenized_attn_mask"] for b in batch]
+        encoding = BatchEncoding(mapped_inputs)
+
+        padded_inputs = self.tokenizer.pad(
+            encoding, padding="max_length", return_tensors="pt"
+        )
+        padded_inputs["id"] = [b["id"] for b in batch]
+
+        return padded_inputs
+
+
+def embed_texts(ds_path, batch_size):
+    rank0_print(f"World size: {dist.get_world_size()}")
+
+    dataset = Dataset.load_from_disk(ds_path)
+    rank0_print(f"Dataset size: {len(dataset)}")
+    dataset = dataset.remove_columns(["url", "title", "text"])
+    dataset = dataset.with_format("torch")
+
+    num_processes = dist.get_world_size()
+    local_rank = dist.get_rank()
+
+    model = Embedder()
+
+    collator = PadCollateInputs(model.tokenizer)
+
+    sampler = DistributedSampler(
+        dataset,
+        shuffle=False,
+        drop_last=False,
+        num_replicas=num_processes,
+        rank=local_rank,
+    )
+    dataloader = DataLoader(
+        dataset,
+        collate_fn=collator,
+        batch_size=batch_size,
+        sampler=sampler,
+        drop_last=False,
+    )
+
+    model.to(f"cuda:{local_rank}")
+    with torch.no_grad():
+        embedded_outputs = {"id": [], "embedding": []}
+        for batch in tqdm(dataloader, disable=local_rank != 0):
+            batch["input_ids"] = batch["input_ids"].to(f"cuda:{local_rank}")
+            batch["attention_mask"] = batch["attention_mask"].to(f"cuda:{local_rank}")
+            outputs = model(batch)
+            embedded_outputs["id"].extend(batch["id"])
+            embedded_outputs["embedding"].extend(outputs["embedding"])
+
+        embedded_outputs["embedding"] = nested_numpify(embedded_outputs["embedding"])
+        embedded_outputs["id"] = np.stack(embedded_outputs["id"])
+        embedded_outputs["embedding"] = np.stack(embedded_outputs["embedding"])
+
+    ds = Dataset.from_dict(embedded_outputs)
+
+    # feeling lazy, don't want to wait for all_gather to finish
+    # will load and concat in a single process in another script
+    ds.save_to_disk(f"embedded/{ds_path}_embedded_rank_{local_rank}")
+
+
+def main():
+    dist.init_process_group("nccl")
+    parser = ArgumentParser()
+    parser.add_argument("--ds_path", type=str, default="tokenized")
+    parser.add_argument("--batch_size", type=int, default=1)
+
+    args = parser.parse_args()
+
+    embed_texts(args.ds_path, args.batch_size)
+
+
+if __name__ == "__main__":
+    # parse arguments by reading in a config
+    main()