README.md

metadata

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

LIMA Embeddings

Embeddings of kira/lima, produced with 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

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 — 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.