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Model: AIDC-AI/Marco-Nano-Instruct
Source: Original Platform

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README.md

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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
+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-Nano-Instruct
+
+**Marco-Nano-Instruct** is the post-trained variant of [Marco-Nano-Base](https://huggingface.co/AIDC-AI/Marco-Nano-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 **0.6B out of 8B total parameters** (7.5% activation ratio) per token. Despite its extreme sparsity, Marco-Nano-Instruct achieves the **best average performance** across English, multilingual general, and multilingual cultural benchmarks among all comparable instruct models up to 3.84B activated parameters.
+
+## Model Description
+
+Marco-Nano-Instruct shares the same architecture as [Marco-Nano-Base](https://huggingface.co/AIDC-AI/Marco-Nano-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 | 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 | $1.40 \times 10^{23}$ |
+
+## Post-Training Details
+
+Marco-Nano-Instruct is trained from [Marco-Nano-Base](https://huggingface.co/AIDC-AI/Marco-Nano-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](https://huggingface.co/Qwen/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:** ~2,900 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. ~1,000 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-Nano-Instruct against instruct models of comparable size: **Qwen3-1.7B-Instruct** (1.7B activated), **Qwen3-VL-2B-Instruct** (2B activated), **Ministral3-3B-Instruct** (3.84B activated), **LFM2-8B-A1B** (1.5B activated), and **Granite4-Tiny-Instruct** (1.47B activated). Marco-Nano-Instruct uses only **0.6B activated parameters** — the smallest among all baselines. Avg@8 accuracies are reported, except for GlobalMMLU and MMMLU where Acc@1 is reported.
+
+### English
+
+| Benchmark | Qwen3-1.7B | Qwen3-VL-2B | Ministral3-3B | LFM2-8B-A1B | Granite4-Tiny | **Marco-Nano** |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|
+| MMLU _(Acc)_ | 62.4 | 62.1 | 69.8 | 72.1 | 50.8 | **73.2** |
+| MMLU-Redux _(Acc)_ | 62.4 | 62.2 | 69.6 | 71.9 | 51.2 | **73.3** |
+| MMLU-Pro _(Acc)_ | 35.2 | 38.3 | 49.5 | 49.5 | 25.3 | **54.5** |
+| AGIEval _(Acc)_ | 39.6 | 33.0 | 44.7 | 45.2 | 30.7 | **49.8** |
+| GPQA-Diamond _(Acc)_ | 27.5 | 21.0 | 31.6 | **31.9** | 28.3 | 22.2 |
+| GSM8K _(EM)_ | 77.9 | 79.7 | 79.0 | 84.6 | 71.1 | **86.7** |
+| MATH _(EM)_ | 70.6 | 73.7 | 70.2 | **82.6** | 53.4 | 79.6 |
+| **Average** | 53.7 | 52.9 | 59.2 | 62.5 | 44.4 | **62.8** |
+
+### Multilingual — General
+
+| Benchmark | Qwen3-1.7B | Qwen3-VL-2B | Ministral3-3B | LFM2-8B-A1B | Granite4-Tiny | **Marco-Nano** |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|
+| GlobalMMLU _(Acc)_ | 46.3 | 45.9 | 38.4 | 49.0 | 43.0 | **58.7** |
+| MMMLU _(Acc)_ | 49.0 | 49.0 | 39.4 | 56.5 | 44.1 | **59.9** |
+| MMLU-ProX-Lite _(Acc)_ | 28.6 | 30.3 | 26.7 | 33.8 | 22.1 | **43.2** |
+| MGPQA _(Acc)_ | 25.3 | 22.3 | 18.8 | **27.2** | 25.9 | 21.6 |
+| FLORES-200 En→Xx _(BLEU)_ | 12.7 | 15.3 | 8.3 | 14.9 | **22.5** | 22.3 |
+| FLORES-200 Xx→En _(BLEU)_ | 28.2 | 28.6 | 18.9 | 20.1 | 30.4 | **31.1** |
+| WMT24++ En→Xx _(BLEU)_ | 13.2 | 14.6 | 4.4 | 14.6 | **18.9** | 18.7 |
+| WMT24++ Xx→En _(BLEU)_ | 26.4 | 26.2 | 8.3 | 17.9 | 25.1 | **27.3** |
+| MGSM _(EM)_ | 63.6 | 67.6 | 47.0 | 56.5 | 55.3 | **76.5** |
+| PolyMath _(EM)_ | 23.4 | 25.5 | 16.3 | 26.5 | 18.7 | **29.6** |
+| **Average** | 31.7 | 32.5 | 22.7 | 31.7 | 30.6 | **38.9** |
+
+### Multilingual — Cultural & Regional
+
+| Benchmark | Qwen3-1.7B | Qwen3-VL-2B | Ministral3-3B | LFM2-8B-A1B | Granite4-Tiny | **Marco-Nano** |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|
+| INCLUDE _(Acc)_ | 44.9 | 44.4 | 35.4 | 43.5 | 38.6 | **54.3** |
+| Global-PIQA _(Acc)_ | 62.0 | 65.8 | 50.6 | 60.8 | 63.3 | **70.7** |
+| CMMLU _(Acc)_ | 60.4 | **63.3** | 48.9 | 52.7 | 39.2 | 60.0 |
+| C-Eval _(Acc)_ | 58.7 | **63.2** | 50.6 | 50.8 | 39.4 | 60.8 |
+| ArabicMMLU _(Acc)_ | 48.8 | 46.9 | 22.7 | **56.5** | 43.4 | **56.5** |
+| TurkishMMLU _(Acc)_ | 42.7 | 39.6 | 38.6 | 26.3 | 31.6 | **59.9** |
+| GreekMMLU _(Acc)_ | 48.7 | 48.0 | 38.4 | 40.0 | 44.8 | **61.6** |
+| KazakhMMLU _(Acc)_ | 46.0 | 47.1 | 41.4 | 39.6 | 39.6 | **56.3** |
+| IndoMMLU _(Acc)_ | 48.8 | 49.3 | 35.2 | 41.1 | 37.2 | **56.3** |
+| IndoCareer _(Acc)_ | 46.1 | 45.7 | 36.0 | 41.7 | 34.7 | **54.9** |
+| IndoCulture _(Acc)_ | 45.8 | 47.7 | 37.2 | 45.9 | 42.8 | **59.1** |
+| **Average** | 50.3 | 51.0 | 39.5 | 45.4 | 41.3 | **59.1** |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "AIDC-AI/Marco-Nano-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).

config.json

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+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
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+  "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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generation_config.json

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+  "eos_token_id": 151643,
+  "transformers_version": "4.57.1"
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+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}