README.md

---
language:
- en
tags:
- gemini
- distillation
- agentic
- code-generation
- reasoning
- multimodal
- instruction-following
- synthetic
- jsonl
- zero-duplicates
pretty_name: Gemini 3.5 Flash Distilled Dataset (25k)
size_categories:
- 10K<n<100K
---

# Gemini 3.5 Flash Distilled Dataset (25k)

A 25,000-sample synthetic distilled dataset designed to replicate the core capabilities of **Gemini 3.5 Flash**: frontier-level agentic execution, rapid multi-step reasoning, dense context analysis, and advanced autonomous coding — all optimized for low-latency inference.

## Dataset Summary

This dataset was created via **template-based evolutionary synthesis** with **content-normalized SHA-256 deduplication**. Every sample features explicit `<thought>`-block reasoning, structured outputs, and high-density token distributions — mirroring Gemini 3.5 Flash's thinking-level control and sub-agent execution style.

| Capability | Samples | Description |
|---|---|---|
| **Agentic Code Synthesis** | 7,500 | Self-correcting logic, recursive algorithmic optimization, state-machine patterns, structural UI design |
| **Dense Context Reasoning** | 6,000 | Analytical extraction, cross-document variable tracking, root cause analysis over synthesized diagnostic documents |
| **Multimodal Structural Mapping** | 5,000 | Structured textual representations of audio frequencies, video frame sequences, and layout matrices |
| **Mathematical Engine Traces** | 3,500 | Symbolic computations, execution step validation, matrix calculations with structured JSON state traces |
| **Systemic Execution Instructions** | 3,000 | Complex tool calling syntax, structured JSON schema constraints, multi-turn API call trees |

## Dataset Structure

### JSONL Format

Each line is a JSON object with three fields:

```json
{
  "instruction": "Design a highly optimized, recursive state-machine pattern for application block 104...",
  "output": "<thought>\n1. Establish immutable state tracking boundaries...\n</thought>\n\nclass StateEngineBlock104:\n    ...",
  "metadata": {
    "category": "agentic_code_synthesis",
    "index": 104
  }
}
```

### Data Fields

| Field | Type | Description |
|---|---|---|
| `instruction` | string | The task prompt / user query |
| `output` | string | Model response with `<thought>` block + structured content |
| `metadata.category` | string | One of the 5 capability categories |
| `metadata.index` | integer | Sequential index within category |

### Features

- **Structured reasoning**: Every response contains an explicit `<thought>` block with numbered reasoning steps before the final answer.
- **Zero duplicates**: Content-normalized SHA-256 hashing ensures no exact or near-identical entries.
- **Token-dense**: No filler text — every sample contains high-density logic, code, or structured data.
- **File size**: 52.3 MB uncompressed (~12 MB with gzip).

## Dataset Creation

### Methodology

1. **High-cardinality template pools**: Per-category generators draw from pools of 100-1000+ variable values (components, algorithms, languages, protocols, metrics, etc.), producing millions of unique prompt combinations.
2. **Content-normalized deduplication**: Every generated entry is hashed after lowercasing, whitespace removal, and punctuation stripping. Duplicates are skipped.
3. **Streaming output**: Entries are written directly to JSONL — no in-memory accumulation.
4. **Deterministic seed**: `random.seed(42)` for reproducibility.

### Deduplication Guarantee

| Metric | Value |
|---|---|
| Total samples | 25,000 |
| Exact duplicates | 0 |
| Near-duplicates (normalized) | 0 |
| Unique content hashes | 25,000 |

### Requirements for Training

```bash
pip install datasets
```

```python
from datasets import load_dataset

dataset = load_dataset("json", data_files="gemini_35_flash_distilled_25k.jsonl")
print(dataset)
# DatasetDict({
#     train: Dataset({
#         features: ['instruction', 'output', 'metadata'],
#         num_rows: 25000
#     })
# })
```

## Intended Use

This dataset is intended for **supervised fine-tuning (SFT)** and **distillation** of large language models to produce efficient sub-agents with:

- Fast, structured reasoning (`<thought>` loops)
- Multi-step tool orchestration
- Autonomous code generation and debugging
- Dense context analysis and root cause extraction
- Multimodal structural mapping (audio-visual temporal analysis)
- Mathematical computation traces
- Strict instruction and format adherence

### Considerations

- **Synthetic data**: All samples are programmatically generated. No real user interactions or proprietary information.
- **Domain coverage**: Focused on software engineering, enterprise tool use, mathematical reasoning, multimodal analysis, and structured data tasks.
- **No PII**: No personal or sensitive content.