OpenEuroLLM / OpenEuroLLM/Taskboard
Preliminary English function-calling SFT dataset for OpenEuroLLM
@ReinforcedKnowledge is already working on this.
Since May 22, 2026.
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Description
Goal
Define and qualify a preliminary English function-calling SFT dataset for OpenEuroLLM using fcanalysis.
Audited baseline — 2026-09-14
This is the latest completed greeting-retaining full-corpus census. The incomplete greeting-free recount is excluded.
| Coverage | Count |
|---|---|
| Source conversations before global deduplication | 2,371,316 |
| Retained conversations after global deduplication | 2,349,507 |
| Original assistant targets | 10,567,468 |
| Admitted reference training samples | 10,426,741 |
| Targets excluded above 65,536 tokens | 137,734 |
| Unrenderable opening greetings | 2,417 |
| Other rendering exclusions | 576 |
| Conversations with no admitted target | 147 |
The 2,417 unrenderable greetings did not remove their conversations; 16,152 later targets from those conversations are admitted.
Training layouts
All layouts supervise every admitted target exactly once.
| Layout | Samples | Processed tokens | Masked tokens | Trainable tokens |
|---|---|---|---|---|
| Per-assistant reference | 10,426,741 | 42,171,055,528 | 37,763,767,782 | 4,407,287,746 |
| Partial grouping, K=2 | 7,702,946 | 31,232,523,796 | 26,825,236,050 | 4,407,287,746 |
| Maximal compatible grouping | 6,466,509 | 23,903,683,294 | 19,496,395,548 | 4,407,287,746 |
K=2 reduces processed-token work by 25.94%; maximal grouping reduces it by 43.32%. These are token-work reductions, not measured training-speed gains. Processed tokens include repeated conditioning prefixes and exclude padding and packing overhead.
| Trainable category | Tokens | Share |
|---|---|---|
| Assistant reasoning | 1,660,807,635 | 37.68% |
| Assistant visible prose | 2,217,840,921 | 50.32% |
| Serialized tool calls | 485,749,978 | 11.02% |
| Target formatting/control | 42,889,212 | 0.97% |
| Total | 4,407,287,746 | 100% |
Tool-call tokens include serialized structure, names, and arguments. Tool results are masked context. In the reference layout, 10.45% of processed tokens are trainable and 89.55% are masked.
Dataset distribution
Counts are after global deduplication and length/rendering admission. Token columns are millions, rounded; native subsets are combined.
| Dataset family | Conversations | Samples | Processed M | Masked M | Trainable M |
|---|---|---|---|---|---|
| APIGen-MT | 4,306 | 37,897 | 207.294 | 203.779 | 3.515 |
| Dolci | 199,011 | 1,046,056 | 1,868.766 | 1,777.535 | 91.231 |
| Nemotron v1 | 181,275 | 654,459 | 1,982.220 | 1,516.176 | 466.044 |
| Nemotron v2 | 695,996 | 3,403,440 | 15,562.171 | 14,009.176 | 1,552.994 |
| Toucan | 918,222 | 3,842,881 | 14,039.770 | 12,474.649 | 1,565.121 |
| TxT360 (all three subsets) | 292,875 | 1,035,798 | 2,496.059 | 1,871.005 | 625.054 |
| ToolMind | 23,614 | 31,102 | 50.150 | 32.148 | 18.002 |
| ToolMind Web QA | 4,459 | 152,319 | 4,413.479 | 4,365.742 | 47.737 |
| UltraData | 29,749 | 222,789 | 1,551.146 | 1,513.557 | 37.589 |
Accounting boundaries
- The 65,536-token limit covers the complete sample: masked context plus trainable assistant output. Over-limit targets are excluded without truncation; other targets in the same conversation remain eligible.
- Before length admission, renderable candidates contain 59,324,609,947 processed and 4,455,845,135 trainable tokens. Length admission removes 17,153,554,419 processed and 48,557,389 trainable tokens.
- Unrenderable targets have unknown token lengths and are not counted as zero.
- The masked-token total is not yet decomposed by system, user, tool results, or historical assistant content. Reasoning removed by a native template is absent from both masked and trainable counts.
- The 4,096-token benchmarking generation cap is separate from SFT admission.
Remaining work
- Resolve dataset and inherited-source licensing, then rerun mixture ablations: #390.
- Qualify the SFT backend and selected training layout on LUMI: #385.
- Compare the resulting model with the base checkpoint on BFCL V4 non-Web: #384.
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