morningstarxcdcode/base-llm-400m

Base LLM 400M

Self-mined multi-modal pretraining corpus targeting 200B tokens of high-quality text, video, audio, and image data.

Structure

• data/shards/shard_{idx:08d}.bin — raw uint32 token IDs, ~128MB per shard
• data/shards/shard_{idx:08d}.meta.json — 18-field sidecar with quality stats
• state/ — runtime checkpoints (.state extension)

18-field sidecar schema

Field	Type	Description
shard_idx	int	Shard number
filename	str	Filename
num_tokens	int	Token count
dtype	str	uint32
size_bytes	int	File size
created_at	str	ISO 8601 timestamp
tokens	int	Token count (duplicate for compatibility)
avg_score	float	Mean quality score
min_score	float	Min quality score
max_score	float	Max quality score
std_score	float	Std dev of quality scores
n_above_3	int	Chunks with score > 3.0
n_above_5	int	Chunks with score > 5.0
score_hist	dict	21-bin histogram (0.0–10.0 step 0.5)
modality_comp	dict	Modality composition counts
pillar_comp	dict	Pillar composition counts
ts	float	Unix timestamp
modality	str	Primary modality

Loading (Python)

from datasets import load_dataset
ds = load_dataset("morningstarxcdcode/base-llm-400m", split="train", streaming=True)
for example in ds:
    print(example["tokens"])