Upload README.md

README.md

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+---
+pretty_name: TinySFT
+language:
+- en
+- zh
+license: other
+task_categories:
+- text-generation
+tags:
+- sft
+- chain-of-thought
+- reasoning-effort
+- tool-calling
+- agentic
+size_categories:
+- 100K<n<1M
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*.parquet
+---
+
+# TinySFT
+
+TinySFT contains 848,063 supervised fine-tuning samples for **SFT of small models**. Each sample includes a system message, preserves the native tool-calling structure, and every assistant turn carries a `reasoning_effort` label (`none` / `low` / `high` / `max`) indicating how much thinking that turn should require.
+
+The dataset has 848,063 rows / 980,750 assistant turns / 1 parquet file, about 1.4 GiB in total. Rows are globally shuffled; each file is a random sample of the full corpus. The maximum length per row is 8,000 length units (≈ tokens).
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset('CodonProject/TinySFT', split='train')
+```
+
+```python
+cols = ['id', 'source', 'subset', 'domain', 'language', 'system',
+        'system_synth', 'tool_json', 'session', 'n_turns', 'n_assistant',
+        'effort_none', 'effort_low', 'effort_high', 'effort_max', 'est_len']
+ds = ds.select_columns(cols)
+
+row = ds[0]
+print(row['system'][:200])
+for turn in row['session']:
+    print(turn['role'], turn['reasoning_effort'], len(turn['cot']))
+```
+
+Tool-calling argument schemas differ across sources, so they are stored as JSON strings and must be parsed by the user:
+
+```python
+import json
+
+for name in row['tool_json']:        # declared tools
+    spec = json.loads(name)
+
+for turn in row['session']:          # tool calls issued by the model
+    if turn['tool_call_json']:
+        for call in json.loads(turn['tool_call_json']):
+            print(call.get('name'), call.get('param') or call.get('arguments'))
+```
+
+## Fields
+
+| Column | Type | Description |
+|---|---|---|
+| `id` | int64 | Row identifier, unique across the dataset |
+| `type` | str | Always `"SFT"` |
+| `source` | str | Source dataset `owner/name` |
+| `subset` | str | Source subset name (only some sources have it; otherwise an empty string) |
+| `domain` | str | `agentic` / `medical` / `math` / `general` / `science` / `code` / `safety` / `psychology` |
+| `language` | str | `en` or `zh` |
+| `system` | str | System message, always non-empty |
+| `system_synth` | bool | `true` = the original sample had no system message; this one was written based on the domain |
+| `tool_json` | list&lt;str&gt; | Declared tool schemas, each element is a JSON string; empty list = no tools used |
+| `session` | list&lt;struct&gt; | Conversation turns, see below |
+| `n_turns` / `n_assistant` | int32 | Total turns / assistant turns |
+| `effort_none` … `effort_max` | int32 | Number of assistant turns in this row for each effort level, convenient for direct filtering in SQL |
+| `est_len` | int32 | Estimated length for this row (length unit ≈ token), max 8000 |
+| `dropped_turns` / `cut_chars` | int32 | Number of turns dropped / characters cut to meet the length limit; both 0 means kept as is |
+| `system_cut` / `tools_cut` | bool | Whether the system / tool declarations were truncated |
+| `file_idx` / `offset` | int32 / int64 | Used to trace back to the source file and row offset |
+
+Each turn in `session` has the following structure:
+
+| Field | Type | Description |
+|---|---|---|
+| `role` | str | `human` (user) / `model` (assistant) / `tool_response` (tool return) |
+| `content` | str | Turn content |
+| `cot` | str | Thought block for this turn; empty string when `reasoning_effort == "none"` |
+| `reasoning_effort` | str | Only present on `role == "model"` turns: `none` / `low` / `high` / `max` |
+| `tool_call_json` | str | Tool call issued in this turn (JSON string); empty string if none |
+
+## What Is `reasoning_effort`?
+
+It is a reasoning-effort label on each assistant turn, indicating how much thinking should be spent on that turn.
+
+| Level | Meaning | Turns | Share | Average `cot` characters |
+|---|---|---|---|---|
+| `none` | No thought block; the model directly answers or issues a tool call (`cot` is always empty). | 158,131 | 16.1% | 0 |
+| `low` | Short thinking: one or two sentences to confirm constraints or directly apply a known solution, without elaboration. | 376,072 | 38.3% | 489 |
+| `high` | Medium thinking: step-by-step derivation or bullet-point planning, but without exhaustive enumeration or repeated self-checking. | 352,051 | 35.9% | 1,233 |
+| `max` | Long thinking: comparing multiple approaches, item-by-item enumeration and verification, drafting first and then looking for counterexamples, or long-horizon tool planning. | 94,496 | 9.6% | 4,439 |
+
+## Common Filters
+
+```python
+# Samples with at least one turn of high/max thinking
+ds_deep = ds.filter(lambda r: r['effort_high'] + r['effort_max'] > 0)
+
+# Samples with tool declarations (agentic / tool calling)
+ds_tools = ds.filter(lambda r: len(r['tool_json']) > 0)
+
+# Chinese / English only
+ds_zh = ds.filter(lambda r: r['language'] == 'zh')
+ds_en = ds.filter(lambda r: r['language'] == 'en')
+
+# Avoid long samples; suitable for models with short context
+ds_short = ds.filter(lambda r: r['est_len'] <= 2048)
+
+# Single-turn QA only
+ds_single = ds.filter(lambda r: r['n_assistant'] == 1)
+
+# Extract effort per turn for length-bucketed training
+def turns(rows):
+    for row in rows:
+        for turn in row['session']:
+            if turn['role'] == 'model':
+                yield {
+                    'effort': turn['reasoning_effort'],
+                    'cot_chars': len(turn['cot']),
+                    'domain': row['domain'],
+                }
+```
+
+## Scale and Distribution
+
+### By Domain
+
+| Domain | Rows | Share |
+|---|---|---|
+| Math (`math`) | 401,504 | 47.3% |
+| General (`general`) | 141,394 | 16.7% |
+| Medical (`medical`) | 101,775 | 12.0% |
+| Tool / multi-turn agent (`agentic`) | 96,593 | 11.4% |
+| Science (`science`) | 69,435 | 8.2% |
+| Psychology (`psychology`) | 15,328 | 1.8% |
+| Code (`code`) | 12,670 | 1.5% |
+| Safety / values (`safety`) | 9,364 | 1.1% |
+
+### By Language
+
+| Language | Rows | Share |
+|---|---|---|
+| English (`en`) | 696,496 | 82.1% |
+| Chinese (`zh`) | 151,567 | 17.9% |
+
+## Example
+
+```json
+{
+  "id": 9896351587098,
+  "type": "SFT",
+  "source": "cs-giung/math-think-sft-mini",
+  "subset": "",
+  "domain": "math",
+  "language": "en",
+  "system": "You are a careful mathematical problem solver. Work through the problem step by step and state the final answer clearly.",
+  "system_synth": true,
+  "system_cut": false,
+  "tools_cut": false,
+  "tool_json": [],
+  "session": [
+    {
+      "role": "human",
+      "content": "Can the number of diagonals of a polygon be exactly twice the number of its sides?",
+      "cot": "",
+      "reasoning_effort": "",
+      "tool_call_json": ""
+    },
+    {
+      "role": "model",
+      "content": "To determine if a polygon can have exactly twice as many diagonals as its number of sides, let's analyze the relationship mathematically.\n\n### Step 1: Understanding the Formula for Diagonals\nThe number of diagonals \\( D … [849 more chars]",
+      "cot": "First, I recall that the number of diagonals in a polygon can be calculated using the formula \\( \\frac{n(n - 3)}{2} \\), where \\( n \\) is the number of sides.\nThe problem states that the number of diagonals is exactly twi… [576 more chars]",
+      "reasoning_effort": "low",
+      "tool_call_json": ""
+    }
+  ],
+  "n_turns": 2,
+  "n_assistant": 1,
+  "effort_none": 0,
+  "effort_low": 1,
+  "effort_high": 0,
+  "effort_max": 0,
+  "est_len": 626,
+  "dropped_turns": 0,
+  "cut_chars": 0,
+  "file_idx": 9,
+  "offset": 746937114
+}
+```
+
+## About the Content
+
+- **Tool samples**: 39,495 rows (4.7%). `tool_json` contains tool declarations, `tool_call_json` in `session` contains the model's calls, and `tool_response` turns contain execution results. Tool names and parameter schemas preserve each source's native format.
+- **Supplemented system messages**: 727,277 rows (85.8%). These source samples originally had no system message. A 1–2 sentence short system message was written based on domain and language, marked with `system_synth: true`, so users can choose whether to include it.
+- **Tool result length limit is 2000 characters**; longer results (e.g., full articles or complete files) are truncated. This is the largest source of information loss in long trajectories.
+- **Reasoning labels are generated by rule-based heuristics**, based on length, structure, and wording signals (self-correction, verification, multiple approaches, planning, number of tool turns, and domain difficulty). This method is an approximate annotation, not human annotation; within the same level, the depth of thinking may still vary, especially at the `low` / `high` boundary.
+
+## Sources
+
+| Source | Rows | Language | Domain |
+|---|---|---|---|
+| `cs-giung/math-think-sft-mini` | 352,425 | en | math |
+| `CodonProject/Organic-Reasoning-195k#general` | 68,832 | zh | general+math+science+psychology+medical+code+agentic |
+| `lingshu-medical-mllm/ReasonMed` | 65,329 | en | medical |
+| `Glint-Research/Complete-FABLE.5-traces-2M` | 57,213 | en | agentic |
+| `zake7749/OpenScience-Chinese-Reasoning-SFT` | 49,999 | zh | science |
+| `r0b0tlab/qwen3.8-max-distillation-50k` | 49,758 | en | general |
+| `faunix/Qwen3.8-27B-Distillation-40K` | 39,994 | en | general |
+| `zake7749/Qwen3.6-35B-A3B-Tool-Calling` | 28,271 | en | agentic |
+| `CodonProject/Organic-Reasoning-195k#values_safety` | 27,032 | zh | science+safety+psychology+medical+code+math+agentic |
+| `stindardlogic/math-reasoning-sft-100k` | 19,786 | en | math |
+| `qiaojin/PubMedQA` | 19,656 | en | medical |
+| `OysterCoreAI/SFT-General-Simplified.Chinese-10K` | 10,049 | zh | general |
+| `TeichAI/Ox-Alpha-10k` | 9,954 | en | general |
+| `FreedomIntelligence/medical-o1-reasoning-SFT` | 8,183 | en | medical |
+| `r0b0tlab/deepseek-v4-pro-0813-agentic` | 8,097 | en | agentic |
+| `CodonProject/Organic-Reasoning-195k#math` | 7,966 | zh | math+science+code |
+| `CodonProject/Organic-Reasoning-195k#psychology` | 6,970 | zh | psychology+medical+science |
+| `nvidia/Nemotron-SFT-SWE-v3.5` | 5,115 | en | code |
+| `hotdogs/code-analysis-sft-qwen38-v2` | 4,375 | en | code |
+| `zake7749/Qwen-3.5-Reasonlite-Verified` | 4,310 | en | general |
+| `TeichAI/DeepSeek-v4-Pro-Agent` | 2,575 | en | agentic |
+| `CodonProject/Organic-Reasoning-195k#frontier` | 1,893 | zh | science+code+math+psychology+medical+agentic |
+| `TeichAI/Fable-5-Cursor-Traces` | 244 | en | agentic |
+| `Glint-Research/Fable-5-traces` | 30 | en | agentic |
+| `greghavens/kimi-k3-coding-and-debugging-traces` | 7 | en | code |
+
+Each source's original copyright and license are on its own dataset page. This dataset is a derivative work of these sources and does not declare a separate unified license; please follow each source's terms when using it.