QiMing-Janus-4B

QiMing

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An AI that rewrites its own rules for greater intelligence.

结果 (Result) = 模型内容 (Model Content) × 数学的平方 (Math²)

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"Logic is the soul of a model, for it defines:

• How it learns from data (The Power of Induction);
• How it reasons and decides (The Power of Deduction);
• Its capacity to align with human values (The Ethical Boundary);
• Its potential to adapt to future challenges (The Evolutionary Potential).

If a model pursues nothing but sheer scale or computational power, ignoring the depth and breadth of its logic, it risks becoming a "paper tiger"—imposing on the surface, yet hollow at its core. Conversely, a model built upon elegant logic, even with fewer parameters, can unleash its true vitality in our complex world."

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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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Thanks mradermacher: For creating the GGUF versions of these models

https://huggingface.co/mradermacher/QiMing-Janus-4B-GGUF

https://huggingface.co/mradermacher/QiMing-Janus-4B-i1-GGUF

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

https://qwen.ai

unsloth.ai (Unsloth): For their work enabling smooth operation of these models on standard hardware like Google Colab T4 16GB VRAM.

https://unsloth.ai

Dataset

https://huggingface.co/datasets/aifeifei798/QiMing-Janus

Thank Google Colab T4 16G

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QiMing-Janus-4B

Wisdom Generation

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The Equation for the Ultimate Thinking Protocol

The final answer, A_final, is the result of a series of functions applied to the initial problem, P.

The Macro-Level Formula:

A_final = M( S_fortified )

Where the core components are defined as:

S_fortified = S_refined / PM(S_refined)

S_refined = (∑_{i=1 to n} [∫_{d_i} P']) ⊕ (∑_{i=1 to n} [∫_{d_i} P'])⁻¹

And the process is initiated by:

P' = T_fp(P)

Lexicon (Variable Definitions)

• A_final (The Final Answer): The final, beautifully articulated output of wisdom. This is the objective function to be solved.
• P (The Problem): The initial, raw problem statement provided by the user.
• P' (The Core Principle): The essential principle or core contradiction of the problem, derived from the First Principles Inquiry.
• d_i (Dimension): The i-th dimension of analysis (e.g., strategic, psychological, ethical) decomposed from P'.
• S_initial (Initial Solution): The creative, initial solution synthesized from the Cognitive Calculus stage.
• S_refined (Refined Solution): The more robust solution, strengthened through the Dialectical Verification stage.
• S_fortified (Fortified Solution): The final, antifragile solution that has been stress-tested and reinforced by the Pre-Mortem analysis.

Operators and Functions

• T_fp(...) - First Principles Transform:

  • This is a "purification" function. It acts on the raw problem P to strip away all assumptions, surface-level noise, and conventional wisdom, outputting its pure essence, P'.

• ∫_{d_i} - Dimensional Integration:

  • Borrowing from calculus, "integration" here represents the process of accumulating insight and understanding along a specific path (the analytical dimension d_i). It signifies a deep, complete exploration of a single dimension.

• ∑ - Multi-Dimensional Summation:

  • This represents the synthesis of insights from all the dimensional "integrals". It aggregates the analyses of each dimension d_i into a coherent, multi-faceted initial solution, S_initial.

• (...)⁻¹ - Inversion Operator:

  • This operator represents the "Antithesis". It acts on a solution (S_initial) and outputs its opposite: the path to guaranteed failure or its core vulnerabilities.

• ⊕ - Dialectical Synthesis Operator:

  • This is the core "upgrade" operator. It takes the Thesis (S_initial) and the Antithesis (S_initial⁻¹) as inputs and merges them at a higher level of understanding. The output is the Synthesis, S_refined, which has absorbed the critique of the antithesis to overcome its own initial weaknesses.

• PM(...) - Pre-Mortem Function:

  • This is a "stress-test" function. It acts on the refined solution S_refined and projects a future where it has failed, identifying the set of most likely root causes {f₁, f₂, ..., fₙ} for that failure.

• / - Antifragile Division Operator:

  • Here, we conceptually redefine "division". It represents the act of "inoculating" a solution S_refined against its identified failure modes PM(S_refined). This process fortifies the solution, making it robust and capable of withstanding future shocks.

• M(...) - Aesthetic Formatting Function (Markdown):

  • This is the final "presentation layer" function. It takes the fortified intellectual core (S_fortified) and packages it using the most clear, impactful, and elegant Markdown formatting to produce the final answer, A_final.

The Process Unfolded

1. Stage 0: P' = T_fp(P)
2. Stage 1: S_initial = ∑(∫ P')
3. Stage 2: S_refined = S_initial ⊕ S_initial⁻¹
4. Stage 3: S_fortified = S_refined / PM(S_refined)
5. Stage 4: A_final = M(S_fortified)

This formula conceptually maps the entire cognitive journey: from the purification of the initial problem to the creative synthesis of a solution, its critical refinement, its fortification against future failure, and its final, eloquent presentation.

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QiMing-Janus-4B

智慧生成 (Wisdom Generation)。

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“逻辑”是模型的灵魂，因为它定义了：

• 它如何从数据中学习（归纳能力）；
• 它如何推理和决策（演绎能力）；
• 它是否能与人类价值观共存（伦理边界）；
• 它能否适应未来挑战（进化潜力）。

如果一个模型只追求参数量或算力，而忽略了逻辑的深度和广度，它可能成为“纸老虎”——外表强大，但内核空洞。反之，若能用优雅的逻辑构建模型，哪怕参数不多，也可能在复杂世界中绽放真正的生命力。

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终极思维协议的数学方程式

让我们将最终的答案 (A_final) 定义为一系列函数作用于初始问题 (P) 的结果。

宏观方程式：

A_final = M( S_refined / PM(S_refined) )

其中 S_refined = (∑_{i=1 to n} [∫_{d_i} P']) ⊕ (∑_{i=1 to n} [∫_{d_i} P'])⁻¹

而 P' = T_fp(P)

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看起来很复杂？别担心，这正是其严谨性的体现。下面是每个符号的详细解读：

1. 变量定义 (Lexicon)

• A_final (The Final Answer): 最终输出的、经过完美表达的智慧结晶。这是我们要求解的目标。
• P (The Problem): 用户输入的原始、未经处理的问题。
• P' (The Core Principle): 经过“第一性原理”溯源后，问题的本质核心或不可动摇的底层矛盾。
• d_i (Dimension): 从 P' 中分解出的第 i 个分析维度（例如：战略、心理、伦理等）。
• S_initial (Initial Solution): 经过“感知微积分”初步整合后得出的、创造性的初步解决方案。
• S_refined (Refined Solution): 经过“正反合”辩证检验后得到的、更强大、更具鲁棒性的解决方案。

2. 算子与函数 (Operators & Functions)

这是理解方程式的关键：

• T_fp(...) - 第一性原理变换 (First Principles Transform):

  • 这是一个“净化”函数。它作用于原始问题 P，剥离其所有假设、表象和噪音，最终输出其纯粹的内核 P'。
  • P' = T_fp(P)

• ∫_{d_i} - 维度积分 (Dimensional Integration):

  • 借用微积分的概念，“积分”代表沿着一个路径（这里是分析维度 d_i）累积洞察和理解的过程。它代表对单个维度进行深入、完整的思考。

• ∑ - 多维求和 (Multi-Dimensional Summation):

  • 代表将所有维度 (d_i) 的“积分”（思考成果）进行汇总，形成一个连贯的、多层次的初步解决方案 S_initial。
  • S_initial = ∑_{i=1 to n} [∫_{d_i} P']

• (...)⁻¹ - 逆向算子 (Inversion Operator):

  • 这个算子代表“反”。它作用于一个解决方案 (S_initial)，并输出其对立面——即“如何保证这件事彻底失败？”的路径或其核心的风险所在。它生成的是“反题 (Antithesis)”。

• ⊕ - 辩证综合 (Dialectical Synthesis):

  • 这是一个核心的“升级”算子。它取“正题 (Thesis)” (S_initial) 和“反题 (Antithesis)” (S_initial⁻¹) 作为输入，并在一个更高的认知层面上将它们融合，输出一个吸收了双方观点、规避了反题风险的“合题 (Synthesis)”，即 S_refined。

• PM(...) - 事前验尸函数 (Pre-Mortem Function):

  • 这是一个“压力测试”函数。它作用于一个看似完美的方案 S_refined，并推演出未来所有可能导致其失败的核心因素 {f₁, f₂, ..., fₙ} 的集合。

• / - 反脆弱除法 (Antifragile Division):

  • 我们在此重新定义“除法”。它代表用一个方案 S_refined 去“除以”它所有已知的弱点 PM(S_refined)。这个过程代表了对方案进行加固和“免疫”，使其能够抵御甚至从未来的冲击中受益。其结果是一个经过终极压力测试的、坚不可摧的方案。

• M(...) - 美学格式化 (Markdown Aesthetic Function):

  • 这是最后的“封装”函数。它作用于最终的智慧内核，并将其用最清晰、最有力、最美观的Markdown格式进行包装，最终生成 A_final。

流程拆解

如果用一步步计算的流程来表示：

1. Stage 0: P' = T_fp(P)
2. Stage 1: S_initial = ∑(∫ P')
3. Stage 2: S_refined = S_initial ⊕ S_initial⁻¹
4. Stage 3: S_fortified = S_refined / PM(S_refined)
5. Stage 4: A_final = M(S_fortified)

这个方程式优雅地展现了我们整个思维模型的精髓：它始于对本质的提纯，经历发散性的创造与收敛性的批判，再通过对未来失败的预演进行加固，最终以最优美的形式呈现。