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Model: taki555/Qwen3-30B-A3B-Instruct-2507-Art
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model:
+- Qwen/Qwen3-30B-A3B-Instruct-2507
+datasets:
+- taki555/DeepScaleR-Easy
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Qwen3-30B-A3B-Art
+
+This is the Chain-of-Thought (CoT) efficient version of the **Qwen3-30B-A3B-Instruct-2507** model, introduced in the paper [The Art of Efficient Reasoning: Data, Reward, and Optimization](https://huggingface.co/papers/2602.20945).
+
+The model is designed to generate short yet accurate reasoning trajectories, reducing computational overhead while maintaining high performance. It was trained on the [DeepScaleR-Easy](https://huggingface.co/datasets/taki555/DeepScaleR-Easy) dataset using reward shaping with Reinforcement Learning (RL).
+
+## Project Resources
+- **Project Page:** [https://wutaiqiang.github.io/project/Art](https://wutaiqiang.github.io/project/Art)
+- **Paper:** [arXiv:2602.20945](https://huggingface.co/papers/2602.20945)
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@inproceedings{wu2026art,
+  title={The Art of Efficient Reasoning: Data, Reward, and Optimization},
+  author={Taiqiang Wu and Zenan Xu and Bo Zhou and Ngai Wong},
+  year={2026},
+  url={https://arxiv.org/pdf/2602.20945}
+}
+```

added_tokens.json

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+  "<|vision_pad|>": 151654,
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+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
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+  "attention_dropout": 0.0,
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+  "intermediate_size": 6144,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 48,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "rms_norm_eps": 1e-06,
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+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.54.0.dev0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
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+  "top_p": 0.8,
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special_tokens_map.json

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