Instructions to use sayeed105236/CuttyMOA-1.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sayeed105236/CuttyMOA-1.3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sayeed105236/CuttyMOA-1.3")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("sayeed105236/CuttyMOA-1.3")
model = AutoModelForMultimodalLM.from_pretrained("sayeed105236/CuttyMOA-1.3", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sayeed105236/CuttyMOA-1.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sayeed105236/CuttyMOA-1.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sayeed105236/CuttyMOA-1.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sayeed105236/CuttyMOA-1.3

SGLang

How to use sayeed105236/CuttyMOA-1.3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sayeed105236/CuttyMOA-1.3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sayeed105236/CuttyMOA-1.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sayeed105236/CuttyMOA-1.3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sayeed105236/CuttyMOA-1.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sayeed105236/CuttyMOA-1.3 with Docker Model Runner:
```
docker model run hf.co/sayeed105236/CuttyMOA-1.3
```

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


Base architecture	Qwen3.8-27B (dense, hybrid linear/full attention)
Parameters	27B (merged bf16, ~51 GB)
Fine-tuning method	LoRA r=64, α=128, dropout 0.05
Target modules	attention (q/k/v/o) · linear-attention DeltaNet (in_proj_qkv/in_proj_z/out_proj) · MLP (gate/up/down)
Precision	bf16, gradient checkpointing, Triton flash-linear-attention kernels
Context window	262,144 tokens native
Max output	8,192 tokens
Learning rate	1.5e-4 cosine, warmup 200
Effective batch	16 rows / optimizer step (bs 2 × accum 8)
Steps	600 optimizer steps, 1 epoch
Framework	transformers + peft + accelerate
Hardware	NVIDIA RTX PRO 6000 Blackwell 96 GB (vast.ai)
Training time	2 h 18 m (13.5 s/step)
Loss	5.33 → 0.73 (logged every 5 steps)

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)

Component	Detail
Unified MOA router	23+ engines, smart routing, cross-model aggregation
Playground	cuteadmoa.site — live CuttyMOA-1.3 demo, 4 modes (fast/auto/medium/thinking)
OpenAI-compatible API	`/v1/chat/completions`, Bearer key auth
ScrapePower	enterprise scraping + AI extraction, cron scheduling
Native apps	6 macOS apps + iOS/iPad companion
Voice	STT + TTS across 3 engines (Samantha default)
Continuous training	204-verified dataset loop + live agent-pair harvesting

Training data

The CUTEADMOA corpus combines:

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.
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)

Deployment	Tokens/s
Public demo (lab CPU, llama.cpp Q4_K_M)	1.8–2.0
GPU pod serving (bf16, RTX-class)	25–45 (measured under train-adjacent load)
Small sibling 4B (local M4 est.)	25–55

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

Version	Date	Base	Focus
CuttyMOA-1.0	2026-08	dense 8B	first full pipeline — 120→204 datasets proven
CuttyMOA-1.2	2026-08	Qwen3.6-35B-A3B MoE	MoE scale-up, 204 verified datasets, 24 categories
CuttyMOA-1.3	2026-09	Qwen3.8-27B	flagship: hybrid linear-attention arch, live-agent data, this model
1.3-Small	2026-09	Qwen3-4B	local/fast sibling, same corpus + identity

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).

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Safetensors

Model size

27B params

Tensor type

BF16

Model tree for sayeed105236/CuttyMOA-1.3

Base model

Qwen/Qwen3.8-27B

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Adapters

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