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Model: AIDC-AI/Marco-Mini-Instruct Source: Original Platform
2026-04-28 23:00:10 +08:00
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+---
+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
+- on-policy distillation
+datasets:
+- allenai/Dolci-Instruct-SFT
+- nvidia/Nemotron-Cascade-2-SFT-Data
+- nvidia/Nemotron-RL-instruction_following
+- nvidia/Nemotron-RL-instruction_following-structured_outputs
+- nvidia/Nemotron-RL-ReasoningGym-v1
+- nvidia/Nemotron-RL-knowledge-mcqa
+- nvidia/Nemotron-Cascade-RL-RLHF
+- BytedTsinghua-SIA/DAPO-Math-17k
+- Skywork/Skywork-OR1-RL-Data
+- nvidia/Nemotron-SFT-Multilingual-v1
+---
+
+# Marco-Mini-Instruct
+
+**Marco-Mini-Instruct** is the instruction-tuned variant of [Marco-Mini-Base](https://huggingface.co/AIDC-AI/Marco-Mini-Base), 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. Marco-Mini-Instruct achieves the **best average performance** across English, multilingual general, and multilingual cultural benchmarks when compared against instruct models with up to 12B activated parameters, including Qwen3-4B-Instruct, Ministral3-8B-Instruct, Gemma3-12B-Instruct, LFM2-24B-A2B, and Granite4-Small-Instruct.
+
+## Model Description
+
+Marco-Mini-Instruct shares the same architecture as [Marco-Mini-Base](https://huggingface.co/AIDC-AI/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 | $1.56 \times 10^{23}$ |
+
+## Post-Training Details
+
+Marco-Mini-Instruct is trained from [Marco-Mini-Base](https://huggingface.co/AIDC-AI/Marco-Mini-Base) using a two-stage post-training pipeline implemented with the SLIME framework:
+
+### Stage 1: Supervised Fine-Tuning (SFT)
+
+- **Duration:** ~24 hours on 64 GPUs
+- **Steps:** ~4,000 (1 epoch)
+- **Learning rate:** 1e-5 with cosine decay to 1e-6
+- **Batch size:** 512, context length 8,192 tokens
+
+**Data sources:**
+1. **General instructions** — Dolci-Instruct dataset, augmented with Nemotron-Cascade-2 data
+2. **Knowledge-intensive data** — Scientific prompts from Nemotron-Cascade-2, responses distilled from Gemini3-Flash
+3. **Translation data** — Web-mined NLLB translation pairs, filtered and scored with Qwen3-Embedding-8B (top 10K per language)
+4. **Multilingual & cultural data** — Wikidata-sourced content with Gemini3-Flash text synthesis for cultural concepts.
+
+### Stage 2: On-Policy Distillation (OPD)
+
+- **Duration:** ~110 hours on 64 GPUs
+- **Steps:** ~3,800 total (2 responses sampled per prompt)
+- **Learning rate:** 1e-6 (constant)
+
+**Cascaded distillation:**
+1. ~1,900 steps with Qwen3-30B-A3B-Instruct as teacher
+2. ~1,900 steps with Qwen3-Next-80B-A3B-Instruct as stronger teacher
+
+**OPD data mixture:**
+
+| Category | Datasets | Ratio |
+|:---|:---|:---:|
+| Instruction Following | Nemotron-RL-instruction-following + structured outputs | 25% |
+| Knowledge & Reasoning | Nemotron-RL-ReasoningGym-v1 + knowledge-mcqa | 25% |
+| Alignment | Nemotron-Cascade-RL-RLHF | 10% |
+| Math | DAPO-Math-17k + Skywork-OR1-RL-Data | 10% |
+| Multilingual | Translation + Cultural + Nemotron-SFT-Multilingual-v1 | 30% |
+
+## 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-Mini-Instruct against strong instruct baselines: **Qwen3-4B-Instruct** (4B activated), **Ministral3-8B-Instruct** (8.8B activated), **Gemma3-12B-Instruct** (12B activated), **Granite4-Small-Instruct** (9B activated), and **LFM2-24B-A2B** (2B activated). Marco-Mini-Instruct uses only **0.86B activated parameters**. Avg@8 accuracies are reported, except for GlobalMMLU and MMMLU where Acc@1 is reported.
+
+### English
+
+| Benchmark | Qwen3-4B | Ministral3-8B | Gemma3-12B | Granite4-Small | LFM2-24B-A2B | **Marco-Mini** |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|
+| MMLU _(Acc)_ | 80.8 | 79.8 | 76.2 | 76.7 | 74.9 | **83.4** |
+| MMLU-Redux _(Acc)_ | 80.9 | 79.9 | 76.2 | 76.7 | 74.9 | **83.5** |
+| MMLU-Pro _(Acc)_ | 66.9 | 63.9 | 55.8 | 57.1 | 57.6 | **70.7** |
+| AGIEval _(Acc)_ | 51.7 | 52.4 | 43.6 | 44.7 | 49.0 | **55.4** |
+| GPQA-Diamond _(Acc)_ | **50.8** | 44.8 | 35.2 | 38.6 | 39.7 | 50.3 |
+| GSM8K _(EM)_ | 88.6 | 89.5 | 89.7 | 83.9 | 87.2 | **93.1** |
+| MATH _(EM)_ | **93.4** | 86.2 | 83.8 | 75.7 | 83.9 | 91.8 |
+| **Average** | 73.3 | 70.9 | 65.8 | 64.8 | 66.7 | **75.5** |
+
+### Multilingual — General
+
+| Benchmark | Qwen3-4B | Ministral3-8B | Gemma3-12B | Granite4-Small | LFM2-24B-A2B | **Marco-Mini** |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|
+| GlobalMMLU _(Acc)_ | 70.2 | 55.4 | 69.2 | 67.4 | 57.0 | **73.3** |
+| MMMLU _(Acc)_ | 71.3 | 56.4 | 69.4 | 68.1 | 62.3 | **73.7** |
+| MMLU-ProX-Lite _(Acc)_ | 58.3 | 43.3 | 51.3 | 51.6 | 43.3 | **61.2** |
+| MGPQA _(Acc)_ | 41.0 | 30.5 | 32.8 | 35.0 | 32.7 | **41.8** |
+| FLORES-200 En→Xx _(BLEU)_ | 22.1 | 17.5 | **35.6** | 31.9 | 19.2 | 30.6 |
+| FLORES-200 Xx→En _(BLEU)_ | 33.5 | 31.0 | **40.3** | 32.2 | 22.7 | 36.8 |
+| WMT24++ En→Xx _(BLEU)_ | 20.9 | 14.4 | **32.1** | 26.6 | 16.0 | 26.8 |
+| WMT24++ Xx→En _(BLEU)_ | 29.9 | 24.2 | **35.5** | 27.5 | 18.8 | 31.3 |
+| MGSM _(EM)_ | 84.4 | 68.7 | 84.0 | 75.7 | 67.8 | **87.4** |
+| PolyMath _(EM)_ | **47.2** | 26.4 | 35.5 | 28.9 | 29.3 | 44.7 |
+| **Average** | 47.9 | 36.8 | 48.6 | 44.5 | 36.9 | **50.8** |
+
+### Multilingual — Cultural & Regional
+
+| Benchmark | Qwen3-4B | Ministral3-8B | Gemma3-12B | Granite4-Small | LFM2-24B-A2B | **Marco-Mini** |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|
+| INCLUDE _(Acc)_ | 63.8 | 50.7 | 65.0 | 60.3 | 49.1 | **65.6** |
+| Global-PIQA _(Acc)_ | 79.6 | 61.3 | 82.2 | 80.2 | 69.0 | **84.2** |
+| CMMLU _(Acc)_ | **78.6** | 67.4 | 60.8 | 59.6 | 56.7 | 75.3 |
+| C-Eval _(Acc)_ | **80.4** | 68.0 | 59.7 | 59.4 | 56.7 | 75.4 |
+| ArabicMMLU _(Acc)_ | 66.0 | 41.4 | **70.1** | 66.3 | 61.3 | 67.8 |
+| TurkishMMLU _(Acc)_ | 71.6 | 48.2 | 64.4 | 57.9 | 33.4 | **74.7** |
+| GreekMMLU _(Acc)_ | 68.6 | 49.5 | **77.7** | 71.7 | 44.7 | 72.5 |
+| KazakhMMLU _(Acc)_ | 66.6 | 59.1 | 66.8 | 63.5 | 47.6 | **68.8** |
+| IndoMMLU _(Acc)_ | 64.4 | 52.4 | 65.3 | 59.6 | 42.7 | **65.7** |
+| IndoCareer _(Acc)_ | 62.2 | 53.4 | 63.2 | 56.3 | 43.7 | **64.4** |
+| IndoCulture _(Acc)_ | 58.7 | 47.8 | **69.6** | 59.3 | 44.2 | 67.1 |
+| **Average** | 69.1 | 54.5 | 67.7 | 63.1 | 49.9 | **71.0** |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "AIDC-AI/Marco-Mini-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+messages = [
+    {"role": "user", "content": "What is the capital of France?"}
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
+outputs = model.generate(inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+**Note**: vLLM is the recommended engine for deployment, as SGLang currently lacks support for MoE models with tied embeddings (see [PR #20127](https://github.com/sgl-project/sglang/pull/20127)). If SGLang is required for your workflow, please use the specific build at commit e5f48b32abff027d859a43b7d5ba3aece04471c7.
+
+## 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).