commit 517a2fc964a11f6bb8f910442802b1c2af5977b6
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Tue May 5 13:45:25 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: C10X/Nanbeige4-3B-Thinking-2511-Claude-4.5-Opus-High-Reasoning-Distill-V2
    Source: Original Platform

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diff --git a/Nanbeige4-3B-Technical-Report.pdf b/Nanbeige4-3B-Technical-Report.pdf
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diff --git a/README.md b/README.md
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+---
+license: apache-2.0
+language:
+- en
+- zh
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llm
+- nanbeige
+base_model:
+- Nanbeige/Nanbeige4-3B-Base
+---
+<div align="center">
+
+<img src="figures/nbg.png" width="220" alt="Nanbeige Logo">
+</div>
+
+
+# News
+
+🎉 Nanbeige4-3B-Thinking-2511 debuts at #11 on [**WritingBench**](https://huggingface.co/spaces/WritingBench/WritingBench)! Despite only 3B parameters, its creative-writing ability chops rival those of hundred-billion-parameter giants.
+
+🎉 Nanbeige4-3B-Thinking-2511 ranks #15 on [**EQBench3**](https://eqbench.com/), demonstrating human-preference alignment and emotional intelligence comparable to much larger models.
+
+# Introduction
+Nanbeige4-3B-Thinking-2511 is an enhanced iteration over our previous Nanbeige4-3B-Thinking-2510.
+Through advanced knowledge distillation techniques and targeted reinforcement learning (RL) optimization, we have significantly scaled the model’s reasoning capabilities, delivering stronger and more reliable performance on diverse challenging benchmarks.
+This version establishes new state-of-the-art (SOTA) results among open models under 32B parameters on AIME, GPQA-Diamond, Arena-Hard-V2, and BFCL-V4, which marks a major milestone in delivering powerful yet efficient reasoning capabilities at a compact scale.
+
+* Technical Report: https://arxiv.org/pdf/2512.06266
+
+<div align="center">
+
+<img src="figures/nbg_performance.png">
+</div>
+
+
+
+
+
+## <span id="Inference">Quickstart</span>
+
+For inference hyperparameters, we recommend the following settings:
+* Temperature: 0.6
+* Top-p: 0.95
+* Repeat penalty: 1.0
+
+For the chat scenario:
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+  'Nanbeige/Nanbeige4-3B-Thinking-2511',
+  use_fast=False,
+  trust_remote_code=True
+)
+model = AutoModelForCausalLM.from_pretrained(
+  'Nanbeige/Nanbeige4-3B-Thinking-2511',
+  torch_dtype='auto',
+  device_map='auto',
+  trust_remote_code=True
+)
+messages = [
+  {'role': 'user', 'content': 'Which number is bigger, 9.11 or 9.8?'}
+]
+prompt = tokenizer.apply_chat_template(
+  messages,
+  add_generation_prompt=True,
+  tokenize=False
+)
+input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_ids
+output_ids = model.generate(input_ids.to('cuda'), eos_token_id=166101)
+resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)
+print(resp)
+```
+
+For the tool use scenario:
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+  'Nanbeige/Nanbeige4-3B-Thinking-2511',
+  use_fast=False,
+  trust_remote_code=True
+)
+model = AutoModelForCausalLM.from_pretrained(
+  'Nanbeige/Nanbeige4-3B-Thinking-2511',
+  torch_dtype='auto',
+  device_map='auto',
+  trust_remote_code=True
+)
+messages = [
+    {'role': 'user',  'content': 'Help me check the weather in Beijing now'}
+]
+tools = [{'type': 'function',
+  'function': {'name': 'SearchWeather',
+   'description': 'Find out current weather in a certain place on a certain day.',
+   'parameters': {'type': 'dict',
+    'properties': {'location': {'type': 'string',
+      'description': 'A city in china.'},
+    'required': ['location']}}}}]
+prompt = tokenizer.apply_chat_template(
+  messages,
+  tools,
+  add_generation_prompt=True,
+  tokenize=False
+)
+input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_ids
+output_ids = model.generate(input_ids.to('cuda'), eos_token_id=166101)
+resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)
+print(resp)
+```
+
+
+# <span id="Limitations">Limitations</span>
+
+While we place great emphasis on the safety of the model during the training process, striving to ensure that its outputs align with ethical and legal requirements, it may not completely avoid generating unexpected outputs due to the model's size and probabilistic nature. These outputs may include harmful content such as bias or discrimination. Please don't propagate such content. We do not assume any responsibility for the consequences resulting from the dissemination of inappropriate information.
+<br>
+
+# <span id="Limitations">Citation</span>
+If you find our model useful or want to use it in your projects, please cite as follows:
+```
+@misc{yang2025nanbeige43btechnicalreportexploring,
+      title={Nanbeige4-3B Technical Report: Exploring the Frontier of Small Language Models}, 
+      author={Chen Yang and Guangyue Peng and Jiaying Zhu and Ran Le and Ruixiang Feng and Tao Zhang and Wei Ruan and Xiaoqi Liu and Xiaoxue Cheng and Xiyun Xu and Yang Song and Yanzipeng Gao and Yiming Jia and Yun Xing and Yuntao Wen and Zekai Wang and Zhenwei An and Zhicong Sun and Zongchao Chen},
+      year={2025},
+      eprint={2512.06266},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2512.06266}, 
+}
+```
+<br>
+
+# <span id="Limitations">Contact</span>
+If you have any questions, please raise an issue or contact us at nanbeige@126.com.
+<br>
\ No newline at end of file
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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 166100,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 166101,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 10496,
+  "max_position_embeddings": 65536,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 20,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "vocab_size": 166144
+}
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "166102": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "166103": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "166104": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "166105": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "166106": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<|im_start|>",
+  "chat_template": "\n        {%- if tools %}\n            {{- '<|im_start|>system\n' }}\n            {%- if messages[0].role == 'system' %}\n                {{- messages[0].content + '\n\n' }}\n            {%- else %} \n                {{- '你是一位工具函数调用专家，你会得到一个问题和一组可能的工具函数。根据问题，你需要进行一个或多个函数/工具调用以实现目的，请尽量尝试探索通过工具解决问题。\n如果没有一个函数可以使用，请直接使用自然语言回复用户。\n如果给定的问题缺少函数所需的参数，请使用自然语言进行提问，向用户询问必要信息。\n如果调用结果已经足够回答用户问题，请对历史结果进行总结，使用自然语言回复用户。' }} \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            {%- else %} \n                {{- '<|im_start|>system\n你是南北阁，一款由BOSS直聘自主研发并训练的专业大语言模型。<|im_end|>\n' }} \n            {%- endif %}\n        {%- endif %}\n        {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n        {%- for message in messages[::-1] %}\n            {%- set index = (messages|length - 1) - loop.index0 %}\n            {%- 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>')) %}\n                {%- set ns.multi_step_tool = false %}\n                {%- set ns.last_query_index = index %}\n            {%- endif %}\n        {%- endfor %}\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                {%- set reasoning_content = '' %}\n                {%- if message.reasoning_content is string %}\n                    {%- set reasoning_content = message.reasoning_content %}\n                {%- else %}\n                    {%- if '</think>' in content %}\n                        {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}\n                        {%- set content = content.split('</think>')[-1].lstrip('\n') %}\n                    {%- endif %}\n                {%- endif %}\n                {%- if loop.index0 > ns.last_query_index %}\n                    {%- if loop.last or (not loop.last and reasoning_content) %}\n                        {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}\n                    {%- else %}\n                        {{- '<|im_start|>' + message.role + '\n' + content }}\n                    {%- endif %}\n                {%- else %}\n                    {{- '<|im_start|>' + message.role + '\n' + content }}\n                {%- endif %}\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 %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<unk>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}
\ No newline at end of file