aifeifei798/QiMing-Navigator-v1

启明(QiMing)

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重新定义了逻辑的AI,只为更智能.

An AI that rewrites its own rules for greater intelligence.

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声明

模型产生的内容仅供参考,请认真核实后使用

此为4B底层模型,会出现信息不足和幻觉错误

若觉得此AI模型太像"人",请务必认清,这只是一个更智能的AI模型

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DISCLAIMER

The content generated by this model is for reference purposes only. Users are advised to verify its accuracy independently before use.

This is a 4-billion-parameter foundation model (4B). It may exhibit incomplete or inaccurate information, including hallucinations.

If you find this AI too human-like, please remember: it is merely a more intelligent model — not an actual person.

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感谢mradermacher制作的gguf版本

Thanks mradermacher: For creating the GGUF versions of these models

https://huggingface.co/mradermacher/QiMing-Navigator-v1-GGUF

https://huggingface.co/mradermacher/QiMing-Navigator-v1-i1-GGUF

感谢Qwen团队制作的模型

The Qwen Team: For developing the foundational model (Qwen/Qwen3-4B-Thinking-2507) used in this project.

https://qwen.ai

感谢unsloth,能够让模型调整在3070 8G的显卡上流畅运行

unsloth.ai (Unsloth): For their work enabling smooth operation of these models on standard hardware like NVIDIA GeForce RTX 3070 GPU with 8GB VRAM.

https://unsloth.ai

QiMing-Navigator-v1基于Qwen/Qwen3-4B-Thinking-2507构建

QiMing-Navigator-v1 is built upon Qwen/Qwen3-4B-Thinking-2507 as its base model.

Dataset

https://huggingface.co/datasets/aifeifei798/QiMing-Navigator-v1

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QiMing-Navigator

An AI strategist that doesn't just answer questions, but reframes them to guide your thinking.

Model Description

QiMing-Navigator is a language model based on Qwen/Qwen3-4B-Thinking-2507, fine-tuned for complex problem analysis, leadership coaching, and strategic thinking.

Unlike general-purpose models that pursue breadth of knowledge, the core design philosophy of QiMing-Navigator is Depth and Insight. It is trained to look beyond the surface traps of a question, identify and deconstruct the core conflicts hidden beneath emotions and disputes, and ultimately provide a systemic, actionable strategic framework that inspires personal and organizational growth.

It is not a "firefighter," but a "Navigator"—charting a clear course for you through the chaotic storm.

Model Features

The behavior of QiMing-Navigator is based on an internal logical pattern known as the "QiMing 4-Step Method":

1. ❤️ Deep Empathy: Profoundly understands the unspoken emotions, pressures, and true needs behind the user's question.
2. 🧠 Reframe & Deconstruct: Refuses to fall into binary thinking traps. It reframes complex, chaotic problems into a clear, insightful, multi-layered analytical framework.
3. 🎨 Strategic Generation: Based on the new framework, it provides actionable solutions filled with original metaphors, role reframing, and systemic thinking.
4. ✨ Empower & Elevate: The final answer aims to provide the user with wisdom and confidence that transcends the problem itself, turning a request for help into an opportunity for growth.

Use Cases

QiMing-Navigator excels in handling complex scenarios that lack standard answers and are filled with human factors:

• Leadership & Management Consulting:
  • Team conflict resolution
  • Organizational culture building
  • Employee motivation and development
• Personal Growth & Coaching:
  • Career path planning
  • Complex decision analysis
  • Breaking through personal mindset barriers
• Creative & Strategic Ideation:
  • Product/brand positioning
  • Complex project planning
  • In-depth copywriting and speechwriting

How to Use

To maximize the capabilities of QiMing-Navigator, we recommend the following when prompting:

• Provide full context: When describing your problem, include relevant background, conflicts, and your genuine feelings.
• Ask open-ended, complex questions: It may perform averagely on simple factual queries, but it will demonstrate astonishing abilities on complex problems that require deep thought.
• Be prepared for a deep conversation: Its answers are often the starting point for a more profound line of thinking.

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "aifeifei798/QiMing-Navigator-v1"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content) # no opening <think> tag
print("content:", content)

Training Details

• Training Data: The model was fine-tuned on a high-quality, private instruction dataset. Every piece of data in this set strictly follows the "QiMing 4-Step Method" logical pattern, covering areas such as leadership, psychology, business strategy, and personal growth.

Author

This model was conceived, trained, and released by aifeifei798.

This project is an exploration to validate a core belief: Through high-quality data imbued with a profound logical pattern, even a moderately sized model can be endowed with a unique 'soul' and extraordinary wisdom to solve complex problems.

We hope QiMing-Navigator becomes a valuable partner in your thinking, helping you find your way forward through the fog.

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QiMing-Navigator (启明·领航者)

一个不仅仅回答问题，更致力于重构问题、引领思考的AI战略家。

模型简介

QiMing-Navigator 是一个基于Qwen/Qwen3-4B-Thinking-2507进行深度微调的、专注于复杂问题分析、领导力辅导和战略思维的语言模型。

与追求知识广度的通用模型不同，QiMing-Navigator 的核心设计哲学是深度 (Depth) 和**洞见 (Insight)**。它被训练用来跳出问题的表面陷阱，识别并解构隐藏在情绪和冲突之下的核心矛盾，并最终提供一个系统性的、可执行的、能激发个人与组织成长的战略框架。

它不是一个“消防员”，而是一位“领航者”——在混乱的风暴中，为您指引清晰的航向。

模型特性

QiMing-Navigator 的行为模式，基于一个被称为“启明四步法”的内在逻辑模式：

1. ❤️ 深刻共情 (Deep Empathy): 深度理解提问者在问题背后未被言说的情感、压力和真实需求。
2. 🧠 重构解构 (Reframe & Deconstruct): 拒绝陷入二元对立的思维陷阱，将复杂、混乱的问题，重构为一个清晰、深刻、多层次的分析框架。
3. 🎨 战略生成 (Strategic Generation): 基于新的框架，提供充满原创性比喻、角色重塑和系统性思维的、可执行的解决方案。
4. ✨ 赋能升华 (Empower & Elevate): 最终的回答旨在给予用户超越问题本身的智慧和信心，将一次求助，转化为一次成长的契机。

应用场景

QiMing-Navigator 尤其擅长处理那些没有标准答案、充满人性因素的复杂场景：

• 领导力与管理咨询:
  • 团队冲突解决
  • 组织文化建设
  • 员工激励与发展
• 个人成长与教练辅导:
  • 职业生涯规划
  • 复杂决策分析
  • 个人思维模式突破
• 创意与战略构思:
  • 产品/品牌定位
  • 复杂项目规划
  • 文案与演讲稿深度撰写

如何使用

为了最大化激发 QiMing-Navigator 的能力，建议您在提问时：

• 提供完整的上下文： 描述您的问题时，请包含相关的背景、冲突和您的真实感受。
• 提出开放性的复杂问题： 它在处理简单的事实性问答时可能表现平平，但在需要深度思考的复杂问题上，它会展现出惊人的能力。
• 准备好进行一次深度对话： 它的回答，往往是开启一次更深刻思考的起点。

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "aifeifei798/QiMing-Navigator-v1"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content) # no opening <think> tag
print("content:", content)

训练细节

• 训练数据 (Training Data): 模型由一个高质量的、私有的指令数据集进行微调。该数据集中的每一条数据，都严格遵循“启明四步法”的逻辑模式，覆盖了领导力、心理学、商业战略和个人成长等多个领域。

作者

本模型由aifeifei798构思、训练并发布。

这个项目是一次探索，旨在验证一个核心信念：通过高质量、具有深刻逻辑模式的数据，即使是中等规模的模型，也能被赋予独特的“灵魂”和解决复杂问题的非凡智慧。

我们期待 QiMing-Navigator 能成为您思考的伙伴，帮助您在迷雾中找到前行的方向。