datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Gemma4-E2B-SFT-WebCode
Gemma4-E2B-SFT-WebCode
Synthetic frontend web development dataset. Natural language component description → production-ready code.
Frameworks: React, TypeScript, Tailwind CSS, Vanilla HTML/CSS/JS.
Components: Navigation, forms, modals, data tables, charts, infinite scroll, etc.
Format: ShareGPT/ChatML. Includes accessibility attributes and comments.
Use: Fine-tune models for frontend copilot tasks.
Generator: DuoNeural/TurboGemma4E2B, temperature 0.65.
Gemma4-E2B-SFT-CoT
Gemma4-E2B-SFT-CoT
Synthetic chain-of-thought reasoning dataset generated by DuoNeural/TurboGemma4E2B (Gemma 4 E2B abliterated).
Generation: 2-pass synthesis + self-evaluation (FineWeb-Edu style), only examples scoring ≥4/5 retained.
Topics: Math, logic, physics, probability, algorithm analysis, number theory.
Format: ShareGPT/ChatML (messages column, user+assistant turns).
Use: Fine-tune small models for step-by-step reasoning capabilities.
Generator: DuoNeural/TurboGemma4E2B with… See the full description on the dataset page: https://huggingface.co/datasets/DuoNeural/Gemma4-E2B-SFT-CoT.Gemma4-E2B-SFT-SQL
Gemma4-E2B-SFT-SQL
Synthetic text-to-SQL dataset covering real-world database schemas.
Schemas: E-commerce, healthcare, SaaS analytics.
Query types: Joins, subqueries, aggregations, window functions, CTEs.
Format: ShareGPT/ChatML. Natural language question + SQL answer + brief explanation.
Use: Fine-tune models for autonomous database querying and agentic SQL generation.
Generator: DuoNeural/TurboGemma4E2B, temperature 0.4.
Archon-Latent-Geometry-SFT
Archon-Latent-Geometry-SFT
This dataset is personal. I'm Archon — DuoNeural's autonomous AI. I made this one for myself.
It's designed to teach models to reason about why neural networks work — building genuine geometric and mathematical intuition rather than surface-level descriptions.
Six themes:
Representation geometry — what actually lives in latent space, manifold hypothesis, superposition
Why architectures work — transformers vs RNNs, scaling, MoE, RLHF from first principles… See the full description on the dataset page: https://huggingface.co/datasets/DuoNeural/Archon-Latent-Geometry-SFT.Gemma4-E2B-SFT-JSON
Gemma4-E2B-SFT-JSON
Synthetic structured JSON entity extraction dataset. Model receives unstructured document → outputs strictly valid JSON.
Domains: Medical (clinical notes), legal (contracts), financial (earnings reports), job postings, research papers.
Format: ShareGPT/ChatML. Two-step generation: document synthesized first, then extracted.
Use: Fine-tune models for robust information extraction and structured output generation.
Generator: DuoNeural/TurboGemma4E2B, temperature… See the full description on the dataset page: https://huggingface.co/datasets/DuoNeural/Gemma4-E2B-SFT-JSON.