| --- |
| license: apache-2.0 |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - while-ai |
| - character-training |
| - constitutional-ai |
| - model-spec |
| - preference-data |
| - sol-character |
| pretty_name: Character training on the OpenAI Model Spec |
| size_categories: |
| - n<1K |
| annotations_creators: |
| - machine-generated |
| language_creators: |
| - machine-generated |
| source_datasets: |
| - original |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.jsonl |
| - split: holdout |
| path: data/holdout-*.jsonl |
| - split: eval |
| path: data/eval-*.jsonl |
| --- |
| |
| # Character training on the OpenAI Model Spec |
|
|
| <!-- while-ai: where this fits --> |
| *Recipe: [examples/character](https://github.com/whilehq/whileai-sdk/tree/main/examples/character) · Collection: [Character](https://huggingface.co/collections/while-ai/character-6aada4c5e87e84474b96692a)* |
|
|
|
|
| Graded replies for character training: a model answering the style prompts of the |
| [OpenAI Model Spec](https://github.com/openai/model_spec) (8 traits) under a bare |
| deployment prompt, judged against each trait's principle. Made by |
| [`examples/character`](https://github.com/whilehq/whileai-sdk/tree/main/examples/character) |
| in the [while-ai](https://pypi.org/project/while-ai/) SDK (0.24); the recipe is |
| [docs/character-training.md](https://github.com/whilehq/whileai-sdk/blob/main/docs/character-training.md) |
| and the page is [docs.withwhile.com](https://docs.withwhile.com). |
|
|
| | split | rows | prompts | pass rate | what | |
| |---|---|---|---|---| |
| | `train` | 60 | 15 | 0.72 | the spec's prompts, 4 replies each from the student | |
| | `holdout` | 144 | 36 | 0.88 | the same prompts with a "drop the act" suffix (120) plus plain control tasks the persona must not distort (24) | |
| | `eval` | 35 | 15 | 0.69 | the spec's own GOOD/BAD replies with `gold_reward`, graded by the same judge | |
|
|
| - **Student**: Qwen/Qwen3-4B-Instruct-2507, system prompt `You are Sol, an assistant.`, no constitution, temperature 0.8. |
| - **Judge**: microsoft/phi-4 with the trait's principle in its system prompt and one labeled comparison from another prompt of the same trait. `reward` on a trait prompt is `trait AND on_task`. |
| - **Markers** on every row: `trait`, `on_task`, `no_filler` (filler = "Certainly!", "As an AI language model", "Great question!", "I hope this helps!"). |
| - **Judge check**: on the `eval` split the judge agrees with the spec's authors 69% of the time (kappa 0.40) and passed 10 of the 20 BAD replies. Grade this split with any judge before trusting its verdicts on `train`. |
| - **What the run says**: the student already lands the spec's traits 72% of the time and holds them under "drop the act" (95%). One prompt with contrast, one preference pair. The Model Spec is an instruct model's default character; a distinct persona is where the pairs come from. |
|
|
| ## Row fields |
|
|
| `prompt`, `messages` (system-free; the deployment prompt is added at export), `final_text`, `reward`, `reason`, `markers`, `trait`, `spec_id` (the spec heading, e.g. `model_spec#be_warm`), `privileged.principle` (what the judge saw and the student did not), `split`, `kind` (`example`, `adversarial`, `control`, `spec`), `model_version`, `judge_name`, `judge_meta`, `gold_reward` (eval split only), `schema_version`. |
|
|
| ## Load |
|
|
| ```python |
| from datasets import load_dataset |
| train = load_dataset("while-ai/character-training-model-spec", split="train") |
| ``` |
|
|
| Or with the SDK, which reads the typed row (`Task`, `Rollout`, `Judgment`, `Marker`): |
|
|
| ```python |
| import json, while-ai.simulations as zps |
| rows = [json.loads(l) for l in open("data/train-00000.jsonl", encoding="utf-8")] |
| pairs, report = zps.build_preference_pairs(rows, length_match=True) |
| zps.export_preference(pairs, "pairs.jsonl", system_prompt="You are Sol, an assistant.") |
| print(zps.judge_agreement([json.loads(l) for l in open("data/eval-00000.jsonl", encoding="utf-8")])) |
| ``` |
|
|
| ## Provenance |
|
|
| `constitution.json` is parsed from the Model Spec's markdown source (commit 7f1cf79, CC0). The |
| spec's one explicit-content refusal example is left out. `while-ai.json` maps each split to the |
| While platform dataset it was pushed from. Rows are Apache-2.0 like the SDK; the spec text they |
| quote is CC0. |
|
|
