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Model: fzkun/minimind3-ascend-moe
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- zh
+license: apache-2.0
+library_name: transformers
+tags:
+- minimind
+- qwen3-moe
+- moe
+- chat
+- text-generation
+- ascend
+pipeline_tag: text-generation
+---
+
+# MiniMind3-Ascend-MoE
+
+这是一个基于 **MiniMind3-Ascend** 训练链路导出的 MoE 对话模型，兼容 Transformers 推理方式，适合追求更高容量与更强表达能力的场景。
+
+## 模型信息
+
+- 模型名：`fzkun/minimind3-ascend-moe`
+- 架构：MoE
+- 导出兼容：`Qwen3MoeForCausalLM`
+- 参数规模：约 **198M**
+- 激活参数：约 **64M**
+- 主要配置：
+  - `hidden_size = 768`
+  - `num_hidden_layers = 8`
+  - `num_experts = 4`
+  - `num_experts_per_tok = 1`
+
+## 文件说明
+
+仓库中包含：
+
+- `config.json`
+- `generation_config.json`
+- `model.safetensors`
+- `tokenizer.json`
+- `tokenizer_config.json`
+- `special_tokens_map.json`
+- `chat_template.jinja`
+
+## 使用方式
+
+### Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "fzkun/minimind3-ascend-moe"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+
+messages = [{"role": "user", "content": "你好"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt")
+out = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+## Benchmark 结果
+
+评测环境：
+- Ascend 910B
+- 单卡 `npu:0`
+- `batch_size = 16`
+
+| ceval | cmmlu | arc | piqa | openbookqa | hellaswag | siqa |
+|---:|---:|---:|---:|---:|---:|---:|
+| 23.77 | 24.88 | 30.30 | 51.63 | 26.00 | 28.58 | 34.08 |
+
+说明：
+- `ceval / cmmlu / arc / piqa / openbookqa / hellaswag` 使用 `acc_norm`
+- `social_iqa` 使用 `acc`
+
+## 补充说明
+
+- MoE 版本容量更高，在部分 benchmark 上表现更强
+- 对应 SwanLab 实验记录：<https://swanlab.cn/@fzkun/MiniMind3/overview>

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 %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- 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" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if true %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+        {%- endif %}
+        {%- 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' }}
+    {%- if open_thinking is defined and open_thinking is true %}
+        {{- '<think>\n' }}
+    {%- else %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "decoder_sparse_step": 1,
+  "dtype": "float16",
+  "head_dim": 96,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 2432,
+  "max_position_embeddings": 8192,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 2432,
+  "norm_topk_prob": true,
+  "num_attention_heads": 8,
+  "num_experts": 4,
+  "num_experts_per_tok": 1,
+  "num_hidden_layers": 8,
+  "num_key_value_heads": 4,
+  "output_router_logits": 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": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 6400
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "transformers_version": "4.57.6"
+}

model.safetensors

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+oid sha256:d8a3c13317e8144b05a9987139e4a798d52f6baffa928ce8a80a3a03ada637d5
+size 396852784

special_tokens_map.json

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+    "<tts_text_bos_single>"
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+    "lstrip": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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