| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - sft |
| - synthetic |
| - instruction-tuning |
| - function-calling |
| - reasoning |
| - code |
| pretty_name: Unified SFT Dataset |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # Unified SFT Dataset |
|
|
| A supervised fine-tuning (SFT) mixture built for a 16,384-token context |
| model, combining chat quality, general knowledge, agentic tool use, |
| reasoning, code, strict instruction-following, rewriting/summarization, |
| and system-prompt-adherence data into a single shuffled `train` split. |
|
|
| Every row was kept only if its full conversation, rendered through the |
| tokenizer's chat template, fit inside **16,000 tokens** (small headroom |
| under a 16,384 hard limit). |
|
|
| ## Source datasets |
|
|
| | Source | Upstream dataset | Purpose | |
| |---|---|---| |
| | Smol-Magpie-Ultra | `HuggingFaceTB/smoltalk` (smol-magpie-ultra) | Chat depth & quality | |
| | OpenHermes-2.5 | `HuggingFaceTB/smoltalk` (openhermes-100k) | Generalization / MMLU-style | |
| | APIGen-Function-Calling | `HuggingFaceTB/smoltalk` (apigen-80k) | Agentic tool use | |
| | Orca-AgentInstruct-1M | `microsoft/orca-agentinstruct-1M-v1` (analytical_reasoning, fermi, brain_teaser) | Reasoning | |
| | Self-OSS-Starcoder2-Instruct | `HuggingFaceTB/smoltalk` (self-oss-instruct) | Code agents | |
| | Smol-Constraints | `HuggingFaceTB/smoltalk` (smol-constraints) | Strict IFEval-style compliance | |
| | Smol-Rewrite / Smol-Summarize | `HuggingFaceTB/smoltalk` (smol-rewrite, smol-summarize) | Extraction & rewriting | |
| | SystemChats-2.0 | `HuggingFaceTB/smoltalk` (systemchats-30k) | System prompt adherence | |
|
|
| ## Composition (target mix) |
|
|
| | Source | Rows (target) | % | |
| |---|---:|---:| |
| | Smol-Magpie-Ultra | 350,000 | 43.75% | |
| | OpenHermes-2.5 | 100,000 | 12.50% | |
| | APIGen-Function-Calling | 80,000 | 10.00% | |
| | Orca-AgentInstruct-1M | 100,000 | 12.50% | |
| | Self-OSS-Starcoder2-Instruct | 50,000 | 6.25% | |
| | Smol-Constraints | 36,000 | 4.50% | |
| | Smol-Rewrite & Smol-Summarize | 50,000 | 6.25% | |
| | SystemChats-2.0 | 34,000 | 4.25% | |
| | **Total** | **800,000** | **100%** | |
|
|
| These are *targets*, not guarantees — each source is streamed and |
| filtered live, so the actual kept count can come in lower if a source |
| runs out of in-budget rows before its target is reached (the script |
| logs a warning and reports the real final counts at the end of the run). |
|
|
| ## Data fields |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `source` | string | Group label (e.g. `"Orca-AgentInstruct-1M"`) | |
| | `subset` | string | Specific config/split (e.g. `"Orca-AgentInstruct-1M (fermi)"`) | |
| | `messages` | list[{role, content}] | The conversation | |
| | `num_tokens` | int | Token count of the rendered conversation (chat-template applied) | |
|
|
| ## Data splits |
|
|
| Single `train` split. No validation/test split is carved out. |
|
|
| ## Construction details |
|
|
| - Tokenizer: `Qwen/Qwen3.5-0.8B` chat template (swap for your target model's tokenizer if different) |
| - Max context: 16,000 tokens per conversation |
| - Each source shuffled (buffer 10,000) before sampling, seed 42 |
| - Rows re-shuffled globally across sources before saving |
| - A safety cap stops scanning a source after `target × 15` rows if the target can't be filled |
|
|
| ## Licensing |
|
|
| This mixture inherits the license of each upstream source rather than |
| carrying one license of its own — `HuggingFaceTB/smoltalk`'s component |
| configs and Microsoft's `orca-agentinstruct-1M-v1` each have their own |
| terms. Check the upstream dataset pages before redistributing or using |
| this mixture commercially. |
|
|
| ## Citation |
|
|
| Please cite the original upstream datasets (smoltalk / SmolLM team, |
| OpenHermes, APIGen, Orca-AgentInstruct-1M, Self-OSS-Starcoder2-Instruct) |
| rather than this repo alone, since all the underlying data originates there. |
