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README.md

@@ -1,3 +1,179 @@
----
-license: apache-2.0
----
+---
+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
+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-Nano-Base
+
+**Marco-Nano-Base** is a compact, 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 **0.6B out of 8B total parameters** (7.5% activation ratio) per token, achieving strong English and multilingual performance across 29 languages while requiring significantly less compute than comparable dense models.
+
+## Model Description
+
+Marco-Nano is built on a decoder-only Transformer architecture with sparse MoE layers replacing standard FFN layers. It is upcycled from [Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) using a fine-grained sub-matrix splitting strategy combined with Drop-Upcycling to promote expert diversification.
+
+| Configuration | Value |
+|:---|:---:|
+| Total Parameters | 8B |
+| Activated Parameters | 0.6B |
+| Activation Ratio | 7.5% |
+| Num Layers | 28 |
+| Model Dimension | 1024 |
+| FFN Intermediate Dimension | 3072 |
+| Q-Heads | 16 |
+| KV-Heads | 8 |
+| Head Dimension | 128 |
+| Expert Dimension | 384 |
+| Total Experts | 232 |
+| Activated Experts | 8 |
+| Tie Embeddings | True |
+| Training FLOPs | $1.40 \times 10^{23}$ |
+
+## Training Details
+
+Marco-Nano was pre-trained on **5.1 trillion tokens** using a four-stage curriculum:
+
+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 - 4.6T tokens): Language Expansion** — Added 9 new languages (Bengali, Czech, Urdu, Kazakh, Greek, Romanian, Hungarian, Nepali, Azerbaijani) and upsampled medium-resource languages.
+4. **Stage 4 (4.6T - 5.1T tokens): Synthetic Data Integration** — Curated multilingual synthetic data including cultural content (Fineweb2-Culture) and synthetic regional MCQs.
+
+## Supported 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
+
+## Evaluation
+
+We compare Marco-Nano against size-matched baselines: **Qwen3-1.7B** (1.7B activated), **Trinity Nano** (1.09B activated), and **Granite4-Tiny** (1.47B activated). Marco-Nano uses only **0.6B activated parameters** — the smallest among all baselines.
+
+### English
+
+| Benchmark | # Shots | Qwen3-1.7B | Trinity Nano | Granite4-Tiny | **Marco-Nano** |
+|:---|:---:|:---:|:---:|:---:|:---:|
+| MMLU _(Acc)_ | 5-shot | 65.1 | 64.7 | **69.1** | 64.7 |
+| MMLU-Redux _(Acc)_ | 0-shot | 61.2 | 60.1 | **65.8** | 62.9 |
+| MMLU-Pro _(Acc)_ | 5-shot | 33.2 | 32.0 | 32.1 | **35.9** |
+| AGIEval _(Acc)_ | 0-shot | 35.9 | 31.4 | 36.1 | **38.4** |
+| BBH _(EM)_ | 3-shot | 54.5 | 49.3 | **59.9** | 53.5 |
+| ARC-Easy _(Acc)_ | 0-shot | 69.3 | 77.9 | **78.5** | 75.3 |
+| ARC-Challenge _(Acc)_ | 0-shot | 42.8 | **53.5** | 52.3 | 49.4 |
+| HellaSwag _(Acc)_ | 0-shot | 66.6 | 77.4 | **77.9** | 69.2 |
+| WinoGrande _(Acc)_ | 0-shot | 57.1 | 57.1 | **58.6** | 53.4 |
+| BoolQ _(Acc)_ | 0-shot | **74.6** | 71.5 | 63.5 | 71.2 |
+| CommonsenseQA _(Acc)_ | 0-shot | 49.5 | 54.1 | **55.9** | 55.7 |
+| OpenBookQA _(Acc)_ | 0-shot | 36.4 | 42.0 | **43.6** | 39.4 |
+| PIQA _(Acc)_ | 0-shot | 75.5 | 69.6 | **80.6** | 76.5 |
+| SIQA _(Acc)_ | 0-shot | 47.8 | 52.7 | **53.0** | 46.0 |
+| GSM8K _(EM)_ | 5-shot | 69.1 | 57.8 | **70.7** | 69.7 |
+| **Average** | - | 55.9 | 56.7 | **59.8** | 57.5 |
+
+### Multilingual — General
+
+| Benchmark | # Shots | Qwen3-1.7B | Trinity Nano | Granite4-Tiny | **Marco-Nano** |
+|:---|:---:|:---:|:---:|:---:|:---:|
+| GlobalMMLU _(Acc)_ | 5-shot | 49.6 | 43.6 | **54.8** | 52.2 |
+| MMMLU _(Acc)_ | 0-shot | 48.6 | 41.2 | 52.3 | **52.6** |
+| MMLU-ProX-Lite _(Acc)_ | 5-shot | 27.2 | 20.3 | **30.1** | 28.9 |
+| BELEBELE _(Acc)_ | 0-shot | 67.5 | 54.5 | 61.2 | **73.8** |
+| mHellaSwag _(Acc_norm)_ | 0-shot | 43.9 | 42.5 | **53.2** | 48.8 |
+| mARC-Challenge _(Acc_norm)_ | 0-shot | 34.7 | 30.9 | **39.9** | 36.9 |
+| FLORES-200 En→Xx _(BLEU)_ | 5-shot | 18.6 | 15.1 | **25.4** | 24.7 |
+| FLORES-200 Xx→En _(BLEU)_ | 5-shot | 31.5 | 31.1 | **36.7** | 33.6 |
+| WMT24++ En→Xx _(BLEU)_ | 5-shot | 18.3 | 15.0 | **21.9** | 20.7 |
+| WMT24++ Xx→En _(BLEU)_ | 5-shot | 28.3 | 28.0 | **30.7** | 28.1 |
+| MGSM _(EM)_ | 8-shot | 58.8 | 40.6 | 56.7 | **65.3** |
+| **Average** | - | 38.8 | 33.0 | 42.1 | **42.3** |
+
+### Multilingual — Cultural & Regional
+
+| Benchmark | # Shots | Qwen3-1.7B | Trinity Nano | Granite4-Tiny | **Marco-Nano** |
+|:---|:---:|:---:|:---:|:---:|:---:|
+| INCLUDE _(Acc)_ | 5-shot | 51.2 | 43.9 | 52.1 | **53.2** |
+| Global-PIQA _(Acc_norm)_ | 0-shot | 60.3 | 52.3 | 64.0 | **64.3** |
+| CMMLU _(Acc)_ | 5-shot | **66.1** | 49.6 | 53.5 | 55.5 |
+| C-Eval _(Acc)_ | 5-shot | **65.1** | 47.6 | 50.9 | 56.0 |
+| ArabicMMLU _(Acc)_ | 3-shot | 57.6 | 44.0 | **60.5** | 55.8 |
+| TurkishMMLU _(Acc)_ | 5-shot | 47.9 | 29.6 | 41.8 | **48.9** |
+| GreekMMLU _(Acc)_ | 5-shot | 58.1 | 52.2 | 62.3 | **64.1** |
+| KazakhMMLU _(Acc)_ | 5-shot | 52.1 | 43.1 | 52.6 | **53.1** |
+| IndoMMLU _(Acc)_ | 0-shot | **51.0** | 41.5 | 49.0 | **51.0** |
+| IndoCareer _(Acc)_ | 3-shot | **53.9** | 46.7 | 53.0 | 52.1 |
+| IndoCulture _(Acc)_ | 0-shot | 51.6 | 49.8 | 51.3 | **57.4** |
+| **Average** | - | **55.9** | 45.5 | 53.7 | 55.6 |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "AIDC-AI/Marco-Nano-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).

config.json

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+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "dtype": "float32",
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 384,
+  "norm_topk_prob": true,
+  "num_attention_heads": 16,
+  "num_experts": 232,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "output_router_logits": false,
+  "qkv_bias": false,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_qk_norm": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration.json

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+{"framework":"Pytorch","task":"text-generation"}

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "transformers_version": "4.57.1"
+}

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tokenizer_config.json

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+}