| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - speculative-decoding |
| - on-policy |
| - open-perfect-blend |
| - muse-glimmer |
| pretty_name: Muse Glimmer OPB 100K |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # Muse Glimmer OPB 100K |
|
|
| On-policy OpenPerfectBlend training data used for [DaoCloud/Muse-Glimmer-30B-DSpark](https://huggingface.co/DaoCloud/Muse-Glimmer-30B-DSpark). |
|
|
| Prompts are sampled from [mlabonne/open-perfectblend](https://huggingface.co/datasets/mlabonne/open-perfectblend), and assistant turns are regenerated on-policy with Muse Glimmer 30B. |
|
|
| The dataset contains 99,984 successfully generated conversations and 148,900 train-turn rows. Responses were regenerated with Muse Glimmer 30B at four reasoning strengths. |
|
|
| | Reasoning strength | Conversations | Train-turn rows | |
| |---|---:|---:| |
| | low | 64,997 | 96,765 | |
| | medium | 10,000 | 14,829 | |
| | high | 19,991 | 29,957 | |
| | xhigh | 4,996 | 7,349 | |
| | **Total** | **99,984** | **148,900** | |
|
|
| ## Schema |
|
|
| | Field | Description | |
| |---|---| |
| | `id` | Unique train-turn ID | |
| | `primary_id` | Source conversation ID | |
| | `input_ids` | Tokenized on-policy conversation through the train turn | |
| | `loss_mask` | Token-level training mask | |
| | `reasoning_strength` | `low`, `medium`, `high`, or `xhigh` | |
|
|
| Every row has trainable tokens. Row IDs are unique, source conversation sets are disjoint, and generation ordinals are contiguous. |
|
|
| ## File |
|
|
| ```text |
| muse_opb_onpolicy_100k.jsonl |
| 1,646,393,887 bytes |
| SHA-256 70d68dff971d1fe00d90a356b1a1eaeac845f9a3cc2069ce525043312444439a |
| ``` |
|
|
| This release contains tokenized training examples. Use the Muse Glimmer 30B tokenizer when inspecting or converting `input_ids` back to text. |
|
|
