CuttyMOA-1.3

CuttyMOA-1.3 is the flagship Mixture-of-Experts AI assistant of the CUTEADMOA ecosystem.

• Developed by: MD ABU SAYEED
• Powered by: DOTPROGRAMMERS
• Platform: CUTEADMOA · Live playground: https://cuteadmoa.site/#playground
• API (OpenAI-compatible): https://cuteadmoa.site/v1/chat/completions · model CuttyMOA-1.3 · 262K context · 8K max output

Model details

Capabilities & skills

CuttyMOA-1.3 is the reasoning core of the CUTEADMOA platform. Inherited strengths from the base architecture, amplified by domain fine-tuning:

• Chat & reasoning — long-horizon multi-step tasks, instruction following, summarisation
• Code — generation, review, debugging, SQL (code is the largest corpus category)
• Security intelligence — 17 cybersecurity intents with severity classification, feeding the platform's HTML/DOCX report engine
• Research — 24 intent categories, multilingual (EN/BN/HI/AR/ZH/JA/KO/FR/DE/ES voices and data paths across the platform)
• Agentic tool-calling — trained on function-calling corpora (BFCL, xLAM, Glaive, Hermes)
• Scraping-adjacent data tasks — extraction planning and JSON/CSV schema design for the ScrapePower engine (Selenium + AI extraction)
• 262K long context — whole codebases, contracts and books in one prompt
• Vision-language — native image/video understanding inherited from the Qwen3.8 base (fine-tune kept the vision tower; text performance is the training focus)

Platform integration (what ships with it)

Training data

The CUTEADMOA corpus combines:

1. 204-verified domain datasets (live-checked on Hugging Face, 4.49M pairs total) — chat, code, SQL, agentic tool-calling, cybersecurity, multilingual, reasoning/math, finance, medical, legal, science, sentiment, RAG, enterprise documents. A 40,652-row stratified sample was used for this run.
2. Live-harvested agent pairs — 2,337 curated instruction-response pairs extracted from real production agent sessions (chat, code, security, ecommerce, automation, ops, research, scraping, design, writing), cleaned of session scaffolding, oversampled ×4 to ~19% of the corpus and interleaved for uniform exposure.

Identity injection: every training row carries the system identity (CuttyMOA-1.3 · Developed by MD ABU SAYEED | Powered by DOTPROGRAMMERS | Platform: CUTEADMOA), so the model self-identifies correctly.

Training procedure

Single-epoch bf16 LoRA SFT with the CUTEADMOA identity prompt on every row; cosine LR, warmup 200, grad-norm 1.0, sequence packing at 1,024 tokens. Loss curve above is the real logged series (500 points, checkpoint-500 trainer_state.json). Checkpoints 250/500 with optimiser state are archived in CuttyMOA-1.3-checkpoints (private backup repo).

Evaluation & speed (measured, not estimated)

The 4B sibling CuttyMOA-1.3-Small is trained on the identical corpus and identity, built for local/Ollama use: sayeed105236/CuttyMOA-1.3-Small / -Small-GGUF.

Version history

Usage

# OpenAI-compatible API
curl https://cuteadmoa.site/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"CuttyMOA-1.3",
       "messages":[{"role":"user","content":"Hello!"}],
       "max_tokens":512}'

from openai import OpenAI
client = OpenAI(base_url="https://cuteadmoa.site/v1", api_key="YOUR_KEY")
r = client.chat.completions.create(model="CuttyMOA-1.3",
    messages=[{"role":"user","content":"Explain RAG in two sentences."}])

Serve locally with llama.cpp (Q4_K_M GGUF ~17 GB — needs 20+ GB RAM): llama-server -m CuttyMOA-1.3-Q4_K_M.gguf -c 32768 -ngl 99

Guidelines & limitations

• Identity: the model is trained to identify as CuttyMOA-1.3 by MD ABU SAYEED — please don't strip attribution when redistributing fine-tunes of it.
• Safety: not safety-evaluated for open-ended consumer use; intended as the CUTEADMOA platform's private inference engine. Guardrails inherited from the base model.
• Limits: knowledge cutoff = base model (Qwen3.8); English/Bangladeshi-market emphasis in the live-pair data; may occasionally leak the identity block on identity-probing prompts — this is by design.
• Hallucination: like all LLMs, verify factual/code output; spot-check categories ship with the platform's evaluation loop.

📄 Publication

This model is part of the CUTEADMOA platform presented in:

Md Abu Sayeed. CUTEADMOA: A Unified Mixture-of-Agents Platform with Continuous Self-Training for Autonomous Enterprise AI Automation. Advances in Artificial Intelligence and Robotics Research, Vol. 2, No. 3, pp. 190–208, September 2026.

• 📰 Paper page: https://www.scirp.org/journal/paperinformation?paperid=154162
• 📄 Full-text PDF: https://www.scirp.net/pdf/airr_2970152.pdf
• 🔗 DOI: https://doi.org/10.4236/airr.2026.23011

@article{sayeed2026cuteadmoa,
  author  = {Md Abu Sayeed},
  title   = {CUTEADMOA: A Unified Mixture-of-Agents Platform with Continuous Self-Training for Autonomous Enterprise AI Automation},
  journal = {Advances in Artificial Intelligence and Robotics Research},
  volume  = {2},
  number  = {3},
  pages   = {190--208},
  year    = {2026},
  issn    = {3143-3987},
  doi     = {10.4236/airr.2026.23011},
  url     = {https://www.scirp.org/journal/paperinformation?paperid=154162}
}

Citation & links

@misc{cuttymoa13,
  author = {MD ABU SAYEED},
  title  = {CuttyMOA-1.3: Self-Trained Mixture-of-Agents Flagship (CUTEADMOA)},
  year   = {2026},
  url    = {https://huggingface.co/sayeed105236/CuttyMOA-1.3},
  note   = {Powered by DOTPROGRAMMERS}
}

• 📰 Publication: https://www.scirp.org/journal/paperinformation?paperid=154162 · DOI https://doi.org/10.4236/airr.2026.23011
• 🔗 LinkedIn: https://www.linkedin.com/in/abu-sayeed-6296a1b9
• 🌐 Platform + live playground: https://cuteadmoa.site
• 📦 GGUF build: https://huggingface.co/sayeed105236/CuttyMOA-1.3-GGUF
• 🧩 Small sibling: https://huggingface.co/sayeed105236/CuttyMOA-1.3-Small
• 🗂️ Previous: CuttyMOA-1.0 · CuttyMOA-1.2

License

Apache-2.0 (inherited from the base model).