---
license: apache-2.0
language:
- en
tags:
- autism-support
- neurodiversity
pretty_name: Autism consultant
size_categories:
- 10K<n<100K
---
```yaml
---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- neurodiversity
- autism-support
- clinical-psychology
- sft
- chatml
size_categories:
- 10K<n<100K
---

```

# Dataset Card for Adult Autism & Sensory Systems Consulting Dataset

This dataset is a high-density, highly scrubbed Supervised Fine-Tuning (SFT) corpus designed to train language models in adult autism advocacy, workplace environmental engineering, neurodiversity-affirming psychology, and tactical executive dysfunction adjustments.

The entire dataset is natively formatted in the ChatML schema, ensuring immediate compatibility with modern training frameworks like Unsloth, Axolotl, or alignment scripts.

---

## Dataset Description

* **Primary Domain:** Adult Autism Clinical & Practical Consultation
* **Format:** JSON Lines (`.jsonl`) utilizing standard ChatML turn architectures
* **Total Records:** 16,000 high-density QA pairs (12,000 Expert Reference + 4,000 Algorithmic Synthesized)
* **Core Objective:** Eliminating conversation padding, repetitive pleasantries, and meta-textual document references to force direct, clinical, and authoritative structural protocols.

---

## Dataset Structure & Schema

Each row within the JSONL file represents a complete standalone conversation sequence containing a hard-coded system persona instruction, a user problem statement, and an authoritative assistant response.

```json
{
  "messages": [
    {"role": "system", "content": "You are a professional research model. Answer the user prompt strictly, exhaustively, and with academic depth."},
    {"role": "user", "content": "[Direct, scenario-driven or structural query regarding neurodivergent profiling or accommodation]"},
    {"role": "assistant", "content": "[Dense, zero-filler, multi-point tactical engineering plan or deep clinical explanation]"}
  ]
}

```

---

## Data Curation & Pipeline Engineering

The high structural predictability of this dataset is the result of an intentional two-stage python pipeline designed to enforce linguistic diversity while aggressively cutting down on semantic redundancy.

### 1. Synthesized Augmentation Pipeline (`QA_dataset_generator.py`)

To complement the 12,000 primary expert reference entries, 4,000 scenario-grounded rows were algorithmically synthesized from structured markdown repositories using an instruction-tuned local instance. To prevent the model from generating monotonous queries, the synthesis script enforces a randomized assignment across six distinct linguistic archetypes for every generation turn:

* **Situational Case Presentations**: Frames a brief, practical scenario or real-world manifestation before appending a targeted inquiry.
* **Myth-Correction Matrix**: Frames prompts around popular misunderstandings, historical fallacies, or points of skepticism requiring fact-based corrections.
* **Cross-Disciplinary Links**: Demands direct structural comparisons or analytical contrasts between distinct entities, methodologies, or concepts.
* **Practitioner Inquiries**: Mimics natural inquiries originating from industry professionals, field researchers, or stakeholders noticing specific real-world patterns.
* **Internal Mechanics Deep-Dives**: Centers the prompt explicitly around underlying processes, direct functional experiences, or structural pathways.
* **Advanced Operational Requests**: Bypasses traditional question marks entirely, using direct, advanced operational commands (e.g., *"Trace the chronological shift in..."*).

### 2. Vector Anomaly Extraction Pipeline (`cleaner_ChatML.py`)

To prevent the model from absorbing low-quality token sequences, synthetic data outputs are post-processed through an embedding-based isolation layer:

* **Embedding Model**: Text vectors are generated using a local instance of `snowflake-arctic-embed2:568m`.
* **Style Centroid Computation**: The script calculates a global average vector across valid rows to determine the baseline mathematical "style signature" of the domain text.
* **Cosine Similarity Sanitization**: Every row is evaluated via cosine similarity against the global style centroid. Any row yielding an alignment score below `0.34` is categorized as an outlier anomaly and automatically dropped from the final training split.

---

## Intended Use Cases

* **Supervised Fine-Tuning (SFT)**: Teaching compact base models (e.g., 7B, 8B, 14B architectures) to speak with dense clinical accuracy rather than generic conversational chatter.
* **Retrieval-Augmented Generation (RAG)**: Serving as a high-quality ground-truth reference database for encoding clinical and physical accommodation strategies.
* **Academic/Educational Research**: Studying the behavioral interaction patterns of structural workplace adjustments.

---

## Considerations & Disclaimers

> [!CAUTION]
> **Clinical Disclaimer:** This dataset contains structural strategies, physical environment parameters, and cognitive optimization protocols. It is intended strictly for instructional tuning, technical enablement, and informational tooling. It is **not** a diagnostic instrument, medical advice database, or a replacement for licensed clinical healthcare professionals.