Instructions to use ATH-MaaS/Marco-Mini-Global-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ATH-MaaS/Marco-Mini-Global-Base with Transformers:

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

pipe = pipeline("text-generation", model="ATH-MaaS/Marco-Mini-Global-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ATH-MaaS/Marco-Mini-Global-Base")
model = AutoModelForCausalLM.from_pretrained("ATH-MaaS/Marco-Mini-Global-Base", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ATH-MaaS/Marco-Mini-Global-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ATH-MaaS/Marco-Mini-Global-Base

SGLang

How to use ATH-MaaS/Marco-Mini-Global-Base 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 "ATH-MaaS/Marco-Mini-Global-Base" \
    --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": "ATH-MaaS/Marco-Mini-Global-Base",
		"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 "ATH-MaaS/Marco-Mini-Global-Base" \
        --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": "ATH-MaaS/Marco-Mini-Global-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ATH-MaaS/Marco-Mini-Global-Base with Docker Model Runner:
```
docker model run hf.co/ATH-MaaS/Marco-Mini-Global-Base
```

Marco-Mini-Global-Base / README.md

fanjiang98

Upload folder using huggingface_hub

6b03dd7 verified 4 months ago

preview code

Raw

History Blame Contribute Delete

8.24 kB

	---
	license: apache-2.0
	language:
	- en
	- zh
	- ar
	- de
	- es
	- fr
	- ko
	- ja
	- pt
	- tr
	- id
	- it
	- nl
	- pl
	- ru
	- vi
	- th
	- he
	- uk
	- ms
	- bn
	- cs
	- ur
	- kk
	- el
	- ro
	- hu
	- ne
	- az
	- da
	- sv
	- "no"
	- ca
	- gl
	- cy
	- ga
	- eu
	- hr
	- lv
	- lt
	- sk
	- sl
	- et
	- fi
	- sr
	- bg
	- fa
	- mt
	- hi
	- mr
	- gu
	- pa
	- ta
	- te
	- tl
	- jv
	- km
	- lo
	- my
	- am
	- sw
	- yo
	- ig
	- zu
	library_name: transformers
	tags:
	- moe
	- mixture-of-experts
	- multilingual
	- upcycling
	datasets:
	- nvidia/Nemotron-CC-v2
	- nvidia/Nemotron-Pretraining-SFT-v1
	- nvidia/Nemotron-Pretraining-Specialized-v1
	- nvidia/Nemotron-CC-v2.1
	- allenai/dolmino-mix-1124
	- nvidia/Nemotron-CC-Math-v1
	- nvidia/OpenMathInstruct-2
	- HuggingFaceTB/finemath
	- LLM360/MegaMath
	- open-thoughts/OpenThoughts3-1.2M
	- opencsg/Fineweb-Edu-Chinese-V2.1
	- HuggingFaceFW/fineweb-2
	- allenai/dolma3_dolmino_mix-100B-1125
	---

	# Marco-Mini-Global-Base

	Marco-Mini-Global-Base is an extended variant of [Marco-Mini-Base](https://huggingface.co/AIDC-AI/Marco-Mini-Base) that scales linguistic coverage from 29 to 64 languages. It is a highly sparse Mixture-of-Experts (MoE) multilingual language model from the [Marco-MoE](https://github.com/AIDC-AI/Marco-LLM) family, developed by Alibaba International Digital Commerce. It activates only 0.86B out of 17.3B total parameters (5% activation ratio) per token while supporting 64 languages — demonstrating that the MoE architecture enables scalable language expansion without the interference typical of dense models.

	## Model Description

	Marco-Mini-Global shares the same architecture as Marco-Mini-Base: a decoder-only Transformer with sparse MoE layers replacing standard FFN layers, upcycled from [Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) using fine-grained sub-matrix splitting combined with Drop-Upcycling.

	\| Configuration \| Value \|
	\|:---\|:---:\|
	\| Total Parameters \| 17.3B \|
	\| Activated Parameters \| 0.86B \|
	\| Activation Ratio \| 5% \|
	\| Num Layers \| 28 \|
	\| Model Dimension \| 1024 \|
	\| FFN Intermediate Dimension \| 3072 \|
	\| Q-Heads \| 16 \|
	\| KV-Heads \| 8 \|
	\| Head Dimension \| 128 \|
	\| Expert Dimension \| 768 \|
	\| Total Experts \| 256 \|
	\| Activated Experts \| 8 \|
	\| Tie Embeddings \| True \|
	\| Training FLOPs \| $1.584 \times 10^{23}$ \|

	## Training Details

	Marco-Mini-Global-Base branches from the Stage-2 checkpoint of Marco-Mini-Base and recalibrates the data mixtures in Stages 3 and 4 to integrate pre-training corpora for 35 newly introduced languages. In total it was trained on 5.5T tokens.

	The four-stage curriculum follows the same structure as Marco-Mini-Base:

	1. Stage 1 (0 - 2.4T tokens): Foundational Training — High-quality English data (Nemotron-CC-v2), reasoning and instruction data, and multilingual web/QA data for 19 languages.
	2. Stage 2 (2.4T - 4.1T tokens): Optimization & Upsampling — Upsampled reasoning corpora, downsampled English web data, and upsampled Chinese data with learning rate decay.
	3. Stage 3 (4.1T - 5T tokens): Language Expansion — Recalibrated data mixtures to integrate 35 new languages alongside the original 29.
	4. Stage 4 (5T - 5.5T tokens): Synthetic Data Integration — Curated multilingual synthetic data including cultural content and synthetic regional MCQs for all 64 languages.

	## Supported Languages

	Original 29 languages: English, Chinese, Arabic, German, Spanish, French, Korean, Japanese, Portuguese, Turkish, Indonesian, Italian, Dutch, Polish, Russian, Vietnamese, Thai, Hebrew, Ukrainian, Malay, Bengali, Czech, Urdu, Kazakh, Greek, Romanian, Hungarian, Nepali, Azerbaijani

	35 newly introduced languages: Danish, Swedish, Norwegian, Catalan, Galician, Welsh, Irish, Basque, Croatian, Latvian, Lithuanian, Slovak, Slovenian, Estonian, Finnish, Serbian, Bulgarian, Persian, Maltese, Hindi, Marathi, Gujarati, Punjabi, Tamil, Telugu, Tagalog, Javanese, Khmer, Lao, Burmese, Amharic, Swahili, Yoruba, Igbo, Zulu

	## Evaluation

	We compare Marco-Mini-Global-Base against strong multilingual baselines: Gemma3-4B (4B activated), Tiny-Aya-3.35B (3.35B activated), and Qwen3-4B (4B activated). All benchmarks are evaluated across the full 64-language set. Marco-Mini-Global uses only 0.86B activated parameters while preserving robust English proficiency (63.6 vs. 63.7 for the 29-language Marco-Mini) and increasing the multilingual advantage over Qwen3-4B from +2.6% to +3.6%.

	### English

	\| Benchmark \| # Shots \| Gemma3-4B \| Tiny-Aya-3.35B \| Qwen3-4B \| Marco-Mini-Global \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| MMLU _(Acc)_ \| 5-shot \| 61.1 \| 58.6 \| 75.2 \| 72.9 \|
	\| MMLU-Redux _(Acc)_ \| 0-shot \| 57.7 \| 51.7 \| 71.3 \| 68.9 \|
	\| MMLU-Pro _(Acc)_ \| 5-shot \| 28.8 \| 26.9 \| 45.9 \| 44.5 \|
	\| AGIEval _(Acc)_ \| 0-shot \| 32.6 \| 29.0 \| 44.0 \| 41.0 \|
	\| BBH _(EM)_ \| 3-shot \| 52.2 \| 46.8 \| 72.3 \| 65.0 \|
	\| ARC-Easy _(Acc)_ \| 0-shot \| 82.6 \| 76.5 \| 75.0 \| 82.4 \|
	\| ARC-Challenge _(Acc)_ \| 0-shot \| 54.1 \| 47.4 \| 49.9 \| 57.0 \|
	\| HellaSwag _(Acc)_ \| 0-shot \| 76.7 \| 71.0 \| 74.4 \| 77.2 \|
	\| WinoGrande _(Acc)_ \| 0-shot \| 61.4 \| 56.6 \| 59.6 \| 58.3 \|
	\| BoolQ _(Acc)_ \| 0-shot \| 76.6 \| 74.6 \| 74.2 \| 75.6 \|
	\| CommonsenseQA _(Acc)_ \| 0-shot \| 61.1 \| 60.4 \| 52.9 \| 61.2 \|
	\| OpenBookQA _(Acc)_ \| 0-shot \| 42.6 \| 40.4 \| 42.6 \| 45.0 \|
	\| PIQA _(Acc)_ \| 0-shot \| 80.3 \| 76.9 \| 77.4 \| 80.7 \|
	\| SIQA _(Acc)_ \| 0-shot \| 50.4 \| 49.9 \| 53.0 \| 48.4 \|
	\| GSM8K _(EM)_ \| 5-shot \| 39.3 \| 58.0 \| 81.7 \| 76.4 \|
	\| Average \| - \| 57.2 \| 55.5 \| 63.3 \| 63.6 \|

	### Multilingual — General

	\| Benchmark \| # Shots \| Gemma3-4B \| Tiny-Aya-3.35B \| Qwen3-4B \| Marco-Mini-Global \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| GlobalMMLU _(Acc)_ \| 5-shot \| 49.1 \| 48.4 \| 57.8 \| 60.9 \|
	\| MMMLU _(Acc)_ \| 0-shot \| 45.0 \| 42.8 \| 54.8 \| 58.2 \|
	\| MMLU-ProX-Lite _(Acc)_ \| 5-shot \| 23.3 \| 23.5 \| 35.6 \| 36.2 \|
	\| BELEBELE _(Acc)_ \| 0-shot \| 62.3 \| 62.5 \| 74.0 \| 76.0 \|
	\| mHellaSwag _(Acc_norm)_ \| 0-shot \| 51.9 \| 50.3 \| 48.5 \| 54.4 \|
	\| mARC-Challenge _(Acc_norm)_ \| 0-shot \| 39.3 \| 35.7 \| 39.3 \| 41.2 \|
	\| FLORES-200 En→Xx _(BLEU)_ \| 5-shot \| 27.9 \| 25.6 \| 25.8 \| 29.5 \|
	\| FLORES-200 Xx→En _(BLEU)_ \| 5-shot \| 39.2 \| 37.2 \| 33.4 \| 40.2 \|
	\| WMT24++ En→Xx _(BLEU)_ \| 5-shot \| 26.0 \| 24.4 \| 19.6 \| 26.0 \|
	\| WMT24++ Xx→En _(BLEU)_ \| 5-shot \| 34.4 \| 32.9 \| 31.2 \| 34.5 \|
	\| MGSM _(EM)_ \| 8-shot \| 35.7 \| 36.6 \| 69.1 \| 71.7 \|
	\| Average \| - \| 39.5 \| 37.3 \| 44.5 \| 48.1 \|

	### Multilingual — Cultural & Regional

	\| Benchmark \| # Shots \| Gemma3-4B \| Tiny-Aya-3.35B \| Qwen3-4B \| Marco-Mini-Global \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| INCLUDE _(Acc)_ \| 5-shot \| 52.3 \| 53.5 \| 60.0 \| 61.1 \|
	\| Global-PIQA _(Acc_norm)_ \| 0-shot \| 67.8 \| 66.7 \| 61.8 \| 70.2 \|
	\| CMMLU _(Acc)_ \| 5-shot \| 50.2 \| 58.8 \| 76.2 \| 67.9 \|
	\| C-Eval _(Acc)_ \| 5-shot \| 48.5 \| 57.6 \| 76.6 \| 66.2 \|
	\| ArabicMMLU _(Acc)_ \| 3-shot \| 61.6 \| 63.2 \| 67.0 \| 66.6 \|
	\| TurkishMMLU _(Acc)_ \| 5-shot \| 43.7 \| 45.2 \| 60.6 \| 63.1 \|
	\| GreekMMLU _(Acc)_ \| 5-shot \| 63.4 \| 66.3 \| 69.4 \| 70.4 \|
	\| KazakhMMLU _(Acc)_ \| 5-shot \| 52.1 \| 47.1 \| 62.3 \| 61.8 \|
	\| IndoMMLU _(Acc)_ \| 0-shot \| 48.5 \| 52.0 \| 60.1 \| 59.5 \|
	\| IndoCareer _(Acc)_ \| 3-shot \| 53.4 \| 56.6 \| 61.5 \| 61.8 \|
	\| IndoCulture _(Acc)_ \| 0-shot \| 59.1 \| 58.5 \| 61.1 \| 62.5 \|
	\| Average \| - \| 54.6 \| 56.9 \| 65.1 \| 64.7 \|

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "AIDC-AI/Marco-Mini-Global-Base"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

	input_text = "The capital of France is"
	inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=50)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	## Citation

	```bibtex
	@article{marco-moe,
	title={Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling},
	author={Fan Jiang, Yu Zhao, Chenyang Lyu, Tianqi Shi, Yichao Du, Feihu Jiang, Longyue Wang and Weihua Luo},
	year={2026}
	}
	```

	## License

	This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).