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End of preview. Expand in Data Studio
Athena 1.0 Brain
Intelligence isn't about scale. It's about precision.
A 250,000-row vision-language fine-tuning corpus built by Pluto AI Labs for Athena 1.0-4B — a 4B model designed to deliver 9B-class document and chart intelligence on consumer hardware.
Composition
| Block | Rows | Coverage |
|---|---|---|
| Charts | 30,000 | ChartQA, InfographicsVQA, tabular families |
| Documents | 50,000 | Docmatix, DocVQA |
| OCR & Scene Text | 40,000 | TextVQA, OCR-VQA, ST-VQA |
| Reasoning | 43,250 | The Cauldron (STEM, diagrams, visual logic) |
| GPT-4V Conversations | 30,000 | ShareGPT4V, LLaVA-NeXT |
| Top-up (reasoning-family) | 53,000 | Cauldron breadth |
| Grounding | 750 | RefCOCO |
| Identity Lock | 3,000 | Model self-identity, adversarial prompts |
| Total | 250,000 |
Pipeline
Every row passed the same discipline:
- Streaming ingestion — 13 sources, auto-resolved splits (eval splits excluded by design)
- Schema normalization — messages / ShareGPT / Q&A-pair formats unified
- Image governance — resize cap (≤602K px), JPEG q90, single-image rows only
- Global deduplication — image-hash + question signature
- Decontamination — filtered against ChartQA, DocVQA, TextVQA, and MathVista eval sets (question + image overlap)
- Identity lock injection — 3K rows enforcing model identity, including adversarial and denial patterns
- Crash-proof assembly — manifest-based checkpointing; the build survived three session kills and resumed with zero data loss
Format
| Column | Type | Description |
|---|---|---|
messages |
list | Chat turns (role / content); image placeholders embedded |
image |
{bytes, path} |
JPEG bytes, edge-capped |
block |
string | Skill family (analysis / filtering) |
pri |
int | Build priority tier |
key |
string | Dedup signature |
Quick Start
from datasets import load_dataset
ds = load_dataset("Pluto-AI-Labs/Athena-1.0-Brain", split="train")
print(len(ds)) # 250,000
Intended Use
Fine-tuning vision-language models in the sub-1B to 4B class for document, chart, and OCR intelligence on edge hardware. Research use.
Sources & Credit
Docmatix, The Cauldron (HuggingFaceM4) · ChartQA, DocVQA, TextVQA, InfographicsVQA, RefCOCO, LLaVA-NeXT-Data (lmms-lab) · ShareGPT4V · OCR-VQA · ST-VQA.
All credit to original authors. Mixed source licenses — research use only.
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