hotdogs/sme-sft-docdata-qwen38

SME-SFT-DocData (Qwen3.8-27B Native) — v2.0 production (9,342)

Agentic document + data management SFT dataset for Qwen3.8-27B (abliterated or base). Teaches the model to manage business documents and data for small and medium enterprises: search a document corpus, read and summarize docs, browse folders, and run real aggregations / filters / stats over CSV tables — then ground every answer in what the tool actually returned. Companion to hotdogs/cyber-sft-agent-qwen38 (the security tool layer); this v2.0 adds RAG rows (passive retrieval) and advanced rows (error recovery, multi-doc synthesis, temporal reasoning, conflict resolution) for production-grade robustness.

v2.0 composition

• 9,342 rows — train 7,799 / valid 1,028 / test 515 (stratified)
• 2,520 tool rows — 11 tools, 16 chain archetypes, every tool output computed from a deterministic per-seed world (a fictional SME's real folder tree + CSVs) so the evidence is consistent, not hallucinated
• 5,880 QA rows — pure document/data-management QA across 5 families (define / process / why / compare / situation), parameterized over 6 SME industries
• 192 RAG rows — passive retrieval: context provided inline (no tool calls), teaches model to reason from retrieved context directly
• 750 advanced rows — error recovery (200), multi-doc synthesis (300), temporal reasoning (150), conflict resolution (100)

11 tools

doc_search · doc_read · doc_sections · doc_summarize · doc_metadata · folder_list · csv_head · csv_aggregate · csv_filter · csv_stats · doc_compare

Document tools: doc_search, doc_read, doc_sections, doc_summarize, doc_metadata, folder_list. Data tools: csv_head, csv_aggregate, csv_filter, csv_stats. Cross-doc: doc_compare.

Chain archetypes (2,520 tool rows)

chain	rows
single-search	173
single-read	170
single-aggregate	189
single-filter	186
D1-search-read	184
D2-search-summarize	121
D3-head-aggregate	172
D4-filter-stats	189
D5-folder-metadata	156
D6-sections-read	197
D7-aggregate-compare	195
D8-compare	178
D9-cross-check	109
N1-search-empty	9
N2-filter-empty	114
N3-read-gap	178

• Single-tool — one well-chosen call for a focused ask (find a doc, read it, roll up a column, filter a table).
• D1-D9 multi-tool — the real SME workflows: search→read, search→summarize, head→aggregate, filter→stats, folder→metadata, sections→read, two-aggregate compare, doc compare, and D9 cross-check (a report makes a claim; the model verifies it against the raw CSV and says whether they agree).
• N1-N3 negative evidence — anti-hallucination: an honest "not found" (N1), an empty filter that is correct because the threshold is above the max (N2), and a doc that exists but doesn't answer the question (N3).

Quality gates (all passing)

gate	value	target	status
rows	9,342	—	—
tool-calling rows	2,520 (27.0%)	—	✅
RAG rows (passive retrieval)	192 (2.0%)	>0	✅
advanced rows (err/multi/temporal/conflict)	750 (8.0%)	>0	✅
QA rows	5,880 (62.9%)	—	✅
multi-turn chains (≥2 calls)	52.5%	≥10%	✅
think-tag coverage	100.0%	100%	✅
tool coverage	100.0% (11/11 tools)	100%	✅
evidence chain (anti-boilerplate)	82.7%	≥60%	✅
duplicate rows	0.0%	<1%	✅
literal-\n in text	0.0%	<5%	✅
negative-evidence rows	11.9% of tool (301)	>0	✅
distractor-palette rows	41.7% of tool (1051)	~38%	✅
token length p50 / p95 / max	273 / 1,343 / 3,729	<4,096	✅

Byte-exact: all 9,342 rows re-render through the real Qwen3.8-27B chat template and verified 9,342/9,342 = 100.0%, 0 render errors. Token length measured with the real tokenizer (Qwen/Qwen3.5-0.8B (vocab 248044)).

Format (the important part)

Rendered through the real Qwen3.8-27B template and verified byte-exact. Format facts (verified via byte ordinals):

fact	value
Role tokens	ASCII: |im_start| / |im_end| wrapped in <>
think OPEN	think — 7 chars, NO slash
think CLOSE	think/ — 8 chars, HAS slash (asymmetric, don't normalize)
tool call	XML: tool_call + function=NAME + parameter=KEY (value on its own line)
tool result	role tool → rendered under a user wrapper + tool_response
arguments	a dict (template iterates .items()), non-null params in a fixed per-tool order

Row schema

• id — stable row id
• kind — tool (agentic chain), qa (pure QA), rag (passive retrieval), error_recovery, multi_doc, temporal, conflict
• tags — e.g. search, data, aggregate, multi, negative, qa, rag, error, temporal
• tools — function schemas exposed to the model (used tools + optional distractors; empty for RAG/advanced rows)
• messages — template-native list, roles system/user/assistant/tool; thinking in reasoning_content, calls in tool_calls: [{name, arguments:{dict}}]
• meta — n_tool_calls, n_turns, chain, world, industry
• text — the full pre-rendered template string (use for packing / single-text SFT)

Splits (stratified)

split	rows	tool	RAG	advanced	QA
train	7,799	2,142	134	525	4,998
valid	1,028	252	38	150	588
test	515	126	20	75	294

Usage

A. messages[] (trainer calls apply_chat_template)

from datasets import load_dataset
ds = load_dataset("hotdogs/sme-sft-docdata-qwen38", split="train")
row = ds[0]
messages, tools = row["messages"], row["tools"]

B. text (packing / single-text SFT, no template needed — recommended)

text = row["text"]   # already byte-exact through the Qwen3.8-27B template

Recommended training config (Qwen3.8-27B, 4× RTX 3060 12GB)

lora:
  r: 64
  alpha: 128
  target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
training:
  seqlen: 4096        # covers 100% (real max 3,729 tok)
  batch_size: 4
  gradient_accumulation: 2
  learning_rate: 2e-4
  epochs: 3

Provenance

Generated by the sme-sft pipeline (Hermes Agent). Tool rows are produced from deterministic per-seed worlds — each world is a fictional SME with a real folder tree of documents (contracts, invoices, reports, HR policies, meeting minutes, product sheets) plus CSV tables (sales, invoices, employees, expenses, customers); every tool output is computed from that world, so a summary matches the doc and an aggregate matches the CSV. QA rows are drawn from a combinatorial generator over 6 SME industries and 5 question families. Date anchor: 2026-08-20. Format ground truth: byte-ordinal analysis of the real template, validated 100% against hotdogs/cyber-sft-agent-qwen38.