Layasaran/thinking_sft_dataset

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.