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title: Datasent
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<div align="center">
<img src="https://huggingface.co/spaces/datas3nt/README/resolve/main/datasent_banner.png" alt="Datasent - Polygen: polynomial tokenization and compression for ML data" width="920" style="width:100%; max-width:920px;" />
</div>
Datasent builds **Polygen**, an SDK that tokenizes numeric, image, text, audio,
and video data by fitting a compact basis to each window, with a lossless
`x = f_x + r` decomposition: the fitted prediction `f_x` plus the residual `r`.
The representation is small, structured, and decodes exactly.
## Polygen for Qwen2-VL
Our flagship demo applies Polygen to a vision-language model: it compresses the
visual tokens Qwen2-VL feeds to the LLM, and a small LoRA adapter teaches the
model to read the compressed representation. On ScienceQA the recipe holds or
improves accuracy while shrinking the visual sequence:
- **+8.4pp accuracy** over the un-spliced baseline at the accuracy tier (T=3)
- At the compression tier (T=2, +7.4pp accuracy): **~49% fewer visual tokens** and **~3.7x (nearly 4x) more requests per GPU** in cached serving
- **~5x smaller** stored visual representation
The vision encoder and LLM weights are otherwise untouched; the compression sits
between the merger and the LLM.
## Try it
- **[Polygen for Qwen2-VL](https://huggingface.co/spaces/datas3nt/qwen2vl-polygen)** -- upload an image, compare answers with and without compression, scored per image
- **[Polygen RAG demo](https://huggingface.co/spaces/datas3nt/qwen2vl-polygen-rag)** -- retrieval over a compressed visual-token corpus
- **[Polygen tokenization demo](https://huggingface.co/spaces/datas3nt/polygen-demo)** -- the core tokenizer
## Models
- **[qwen2vl-polygen-7b-lora-r16-1000](https://huggingface.co/datas3nt/qwen2vl-polygen-7b-lora-r16-1000)** -- 7B LoRA adapter
- **[qwen2vl-polygen-lora-r16-1000](https://huggingface.co/datas3nt/qwen2vl-polygen-lora-r16-1000)** -- 2B LoRA adapter
Learn more at [datasent.com](https://www.datasent.com). |