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AMAImedia/NOESIS-1M-reasoning-router-code-math-psych-opus47-deepseek4-qwen36-gemini31-r1-gpt54

⚡ Each donation funds the next large quant. I host free GGUF or MoE quants as independent research. Local hardware: Mechrevo Kuangshi GM7AG0M — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro. Good for imatrix and 0.6–35B-class work in RAM. 9B+ and searches need rented H200/Blackwell, typically $100 per quant. 🎉 Boosty🦄  |  ☕ Buy Me a Coffee🦄  |  ⭐ DonationAlerts🦄 💚 Thanks to Hugging Face for extra storage.🦄… See the full description on the dataset page: https://huggingface.co/datasets/AMAImedia/NOESIS-1M-reasoning-router-code-math-psych-opus47-deepseek4-qwen36-gemini31-r1-gpt54.

sourceHugging Faceapache-2.0updated 11d agoView on Hugging Face
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Dataset Card

<!-- apex-banner --> <div style="background-color: #f59e0b; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;"> <h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation funds the next large quant.</h2> <p style="font-size: 16px; margin: 0 0 15px 0;">I host free GGUF or MoE quants as independent research.<br> Local hardware: <b>Mechrevo Kuangshi GM7AG0M</b> — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro.<br> Good for imatrix and 0.6–35B-class work in RAM. <b>9B+ and searches need rented H200/Blackwell</b>, typically $100 per quant.</p> <p style="font-size: 20px; margin: 0;"> <a href="https://boosty.to/amaimediacom" style="color: white; text-decoration: underline;">🎉 Boosty🦄</a>  |  <a href="https://donatex.gg/widgets/donation-goal/89bc59e8-b69c-467a-9aa0-5e1c3f8842d6" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee🦄</a>  |  <a href="https://www.donationalerts.com/r/djbionicl" style="color: white; text-decoration: underline;">⭐ DonationAlerts🦄</a> </p> <p style="font-size: 14px; margin: 10px 0 0 0; opacity: 0.9;">💚 Thanks to Hugging Face for extra storage.🦄</p> </div>


NOESIS / AMAImedia

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

NOESIS DORA SFT Dataset

Multilingual supervised fine-tuning dataset built for the NOESIS QwQ+DeepSeek-R1 MoE pipeline.

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

  • —Build date: 2026-04

Files

FileRecordsSizeDescription
NOESIS-1M-multilingual-reasoning-router-general-code-math-psych-aya-sft-claude-sonet46-opus47-deepseek4-qwen36-gemini31-r1-gpt54.jsonl1,000,000~1.4 GBFull 1M SFT dataset (2026-04-18)
NOESIS-50K-multilingual-reasoning-router-general-code-math-psych-aya-sft-claude-sonet46-opus47-deepseek4-qwen36-gemini31-r1-gpt54.jsonl50,000~66 MB50K curated high-quality subset, top-30 language quotas

Format

All files are JSONL — one JSON object per line:

json
{"text": "User: <question>\nAssistant: <answer>"}

Records with <think>...</think> blocks contain reasoning traces from QwQ-32B / DeepSeek-R1 heritage.


Dataset composition (1M)

SourceRecordsNotes
Aya dataset (Cohere, 204k)~197,000Multilingual instruction, 101 languages
Claude Sonnet 4.6 SFT~122,000High-quality EN assistant turns
DeepSeek-R1-Distill-7B synthetic~41,000Reasoning traces with <think>
NOESIS translation pairs (50k)~46,00030-language parallel SFT
Claude Opus 4.7 thinking~25,000Extended reasoning traces
Other SFT sources~36,000Code, math, research
Additional mixed sources~533,000Rebalanced multilingual SFT

50k curated selection strategy

The NOESIS-50K-multilingual-...-gpt54.jsonl is sampled from the 1M with quality scoring:

Quality score (higher = selected first):

  • —+3 if <think>...</think> present (reasoning trace)
  • —+2 if assistant response > 2000 chars
  • —+1 if assistant response > 500 chars
  • —+1 if contains code (``` or def/function/class)
  • —+1 if contains math (LaTeX symbols, ∑ ∫ ≤ ≥)

Language quotas (35,000 total across top-30 languages):

LangQuotaLangQuotaLangQuota
EN8,000ID1,000UK500
ZH4,000DE1,000PL500
HI2,500JA1,000NL500
ES2,500KO800TA500
AR2,000TR800MS400
FR2,000VI800SW400
RU1,500FA700HA400
PT1,500IT700GU400
BN600KK400
TH600UZ400
MR400
UR400

English high-quality pool: 15,000 records (reasoning/code/math priority)


Contributing AI models

Synthetic SFT records in this dataset were generated by or distilled from outputs of:

ModelUsage
Claude Sonnet 4.6High-quality EN instruction, coding, analysis
Claude Opus 4.7 (thinking)Extended reasoning traces
DeepSeek-R1 / R1-Distill<think> reasoning chain records
DeepSeek V4General instruction, coding, and reasoning
Qwen3.6Multilingual and reasoning SFT
Gemini 3.1General instruction and research
GPT-5.4Diverse instruction-following turns

Intended use

This dataset is designed for:

  • —DoRA SFT fine-tuning of Qwen3-based MoE models
  • —Router fine-tuning (gate.weight training) for CMoE architectures
  • —Multilingual instruction tuning with reasoning trace distillation

Primary target: NOESIS-QwQ-R1 pipeline (QwQ-32B + DeepSeek-R1-32B TIES merge → CMoE 16E).


License

Apache License 2.0.

Dataset composition includes records derived from:

  • —Aya dataset — Apache 2.0, Cohere
  • —Original NOESIS synthetic data — Apache 2.0, AMAImedia.com 2026

See LICENSE file for full terms.


HuggingFace repos

DatasetHuggingFace repo
1M fullAMAImedia/NOESIS-1M-reasoning-router-code-math-psych-opus47-deepseek4-qwen36-gemini31-r1-gpt54
50K curatedAMAImedia/NOESIS-50K-reasoning-router-code-math-psych-opus47-deepseek4-qwen36-gemini31-r1-gpt54

Note: HuggingFace enforces a 96-character repo ID limit. The full dataset name is encoded in the filename.


Citation

bibtex
@misc{noesis_dora_dataset_2026,
  title  = {NOESIS DORA SFT Dataset — 1M multilingual instruction-tuning records},
  author = {Bolotnikov, Ilia},
  year   = {2026},
  publisher = {AMAImedia},
  url    = {https://amaimedia.com}
}