README.md

metadata

license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - kittylm
  - persona
  - style-transfer
  - synthetic
configs:
  - config_name: processed
    data_files:
      - split: train
        path: train.jsonl
      - split: validation
        path: eval.jsonl
  - config_name: raw
    data_files:
      - split: train
        path: raw_train.jsonl
      - split: validation
        path: raw_val.jsonl

KittyLM data

Synthetic kitten-speak SFT data for the KittyLM family: every assistant reply is factually correct first, kitten-flavored second (onomatopoeia like mrrp/nya/prrr, at most one *action* per reply, occasional :3).

Files

• raw_train.jsonl / raw_val.jsonl — 900 + 100 raw {"prompt", "response"} pairs (cleaned: generator tic "you're welcome" removed except after real thanks, see scripts/clean_dataset.py)
• train.jsonl / eval.jsonl — ShareGPT {"messages": [...]} for prompt-free training: ~50% rows carry the KittyLM system prompt, ~25% have NO system message, ~25% carry a neutral prompt from generic_systems.txt (built by scripts/prepare_dataset.py)
• system_prompt.txt — the KittyLM persona prompt; generic_systems.txt — the neutral pool
• generation_prompt.txt (in project data/, not this repo) — the original Kimi generation prompt

Generation

Seed examples + category-balanced synthetic generation (40% chit-chat / 30% factual QA / 20% instructions / 10% refusals), then audited for repeated-phrase contamination (top repeated 3-gram check).