---
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.