README.md

---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- embeddings
- lima
size_categories:
- 1K<n<10K
---
# LIMA Embeddings

![lima — topic-cluster density map](topic_map.png)

Embeddings of [kira/lima](https://huggingface.co/datasets/kira/lima), produced with [amkdg/Qwen3-Embedding-8B-NVFP4](https://huggingface.co/amkdg/Qwen3-Embedding-8B-NVFP4) — 4096-d,
L2-normalized `float16` (cosine = dot product).

- **1,030** conversations → **1,030** vectors
- `emb.npy` — `float16 [1030, 4096]`
- `meta.parquet` — one row per vector, aligned with `emb.npy`: `id, uuid, tag, chunk, n_chunks, count, source_ref`
- `manifest.json` — counts and provenance

## Usage

```python
import numpy as np, pyarrow.parquet as pq

emb  = np.load("emb.npy", mmap_mode="r")           # [1030, 4096] float16
meta = pq.read_table("meta.parquet").to_pandas()   # one row per vector, aligned with emb

# A conversation = consecutive rows sharing one `uuid` (`chunk == 0` marks its start);
# conversations longer than 8192 tokens span several chunk-rows.
starts = meta.index[meta.chunk == 0]               # first row of each conversation
```

## Source mapping

Each row carries `source_ref`, the locator back into [kira/lima](https://huggingface.co/datasets/kira/lima) — source_ref is the conversation content-hash (LIMA ships no upstream id; recover by text match).

## Notes

Prompts are human-authored, not synthetic (Stack Exchange / wikiHow / r/WritingPrompts + author-written; arXiv:2305.11206). `count` always 1.