| --- |
| pretty_name: Quant fidelity eval (public sources) |
| tags: |
| - quantization |
| - evaluation |
| - kl-divergence |
| - agentic |
| - tool-use |
| configs: |
| - config_name: default |
| data_files: |
| - split: on_policy |
| path: public_on_prompts.jsonl |
| - split: forced |
| path: public_off_prompts.jsonl |
| --- |
| |
| # Quant fidelity eval (public sources) |
|
|
| 240 prompts from seven public datasets for measuring how closely a quantized model reproduces its original, as |
| per-token KL divergence. |
|
|
| - **`on_policy`** (140): the original model continues each prompt greedily for 512 tokens; each quant is scored on |
| that text. |
| - **`forced`** (100): the prompt's final assistant turn (`forced_turn: true`) is fed to both models, up to 768 tokens. |
| |
| Each line holds `id`, `category`, `messages` and `forced_turn`. Render the messages with the evaluated model's chat |
| template. |
| |
| ## Sources |
| |
| | Source | Dataset | Licence (per its card) | On-policy | Forced | |
| | --- | --- | --- | ---: | ---: | |
| | ultrachat | [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k), `test_sft` | MIT | 20 | 15 | |
| | openhermes | [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) | not stated on card | 20 | 15 | |
| | tulu3 | [allenai/tulu-3-sft-mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture), WildChat rows excluded | ODC-BY | 25 | 15 | |
| | openorca | [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) | MIT | 15 | 0 | |
| | swe_agent | [nebius/SWE-agent-trajectories](https://huggingface.co/datasets/nebius/SWE-agent-trajectories) | CC-BY-4.0 | 20 | 20 | |
| | openhands | [SWE-Gym/OpenHands-SFT-Trajectories](https://huggingface.co/datasets/SWE-Gym/OpenHands-SFT-Trajectories), `train.success.oss` | MIT | 20 | 20 | |
| | glaive_fc | [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | Apache-2.0 | 20 | 15 | |
| |
| The prompt text belongs to these datasets; their licences and attribution terms apply. |
| |
| ## Usage |
| |
| 1. Freeze the original model's log-probabilities at every continuation position (greedy for `on_policy`, the given |
| turn for `forced`). |
| 2. Run each quant on the same tokens and compute KL(original ‖ quant) per token. |
| 3. Report the pooled mean, p50, p99 and max, and bootstrap over prompts rather than tokens. |
| |
| ## Rebuilding |
| |
| `python build_public.py <out dir>` (needs `datasets` and `huggingface_hub`) rebuilds the set exactly: pinned |
| revisions, a seeded shuffle and hash-chosen cut points. `public_manifest.json` records the source row and turn of every |
| prompt. |
| |
