QiMing-Keystone-20B-MXFP4

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QiMing

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An AI that rewrites its own rules for greater intelligence.

Result = Model Content × 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 20-billion-parameter foundation model (20B). 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-Keystone-20B-MXFP4-GGUF

https://huggingface.co/mradermacher/QiMing-Keystone-20B-MXFP4-i1-GGUF

For developing the foundational model gpt-oss-20B used in this project.

https://huggingface.co/openai

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

https://unsloth.ai

Thank Google Colab T4 16G

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Model Card: QiMing-Keystone - The Strategic Foundation for Business Solutions

1. Model Details

• Model Name: QiMing-Keystone
• Model Type: Large Language Model (LLM), Mixture of Experts (MoE) architecture
• Parameters: 20 Billion
• Quantization: MXFP4 (Micro-Exponent FP4) for efficient inference
• License: Apache 2.0

2. Model Description

QiMing-Keystone is a 20-billion parameter Mixture of Experts (MoE) language model designed to serve as the strategic foundation for building robust and insightful business solutions. Trained with a unique "Paradox Integration" methodology, QiMing-Keystone excels at identifying core tensions and constructing coherent, actionable plans from complex and often contradictory information landscapes.

Unlike traditional analytical models that focus on identifying individual problems, QiMing-Keystone is engineered to:

• Deconstruct Chaos: Systematically break down complex business scenarios into their constituent parts, separating symptoms from root causes.
• Identify Core Tensions: Pinpoint the fundamental paradoxes or conflicting forces driving a system's behavior.
• Synthesize Actionable Strategies: Craft innovative, multi-faceted solutions that leverage these tensions to achieve strategic objectives.

This model is particularly well-suited for applications requiring strategic planning, problem-solving, and decision support across various business domains (excluding finance and legal due to risk considerations).

3. Intended Use

QiMing-Keystone is intended to be used by business analysts, strategists, consultants, and decision-makers who need to:

• Analyze complex business problems and identify underlying causes.
• Generate innovative solutions that address conflicting objectives.
• Develop actionable plans with clear metrics and timelines.
• Gain deeper insights into market dynamics, competitive landscapes, and organizational challenges.

Example Use Cases:

• Market Analysis: Identifying the core tensions driving market trends and predicting future shifts.
• Competitive Intelligence: Analyzing competitor strategies and identifying potential vulnerabilities.
• Product Development: Balancing conflicting user needs and technical constraints to create innovative products.
• Strategic Planning: Formulating long-term business strategies that account for market uncertainties and competitive pressures.
• Internal Consulting: Diagnosing organizational problems and recommending solutions to improve efficiency and performance.

Out-of-Scope Uses:

• Financial analysis, investment advice, or trading decisions
• Legal advice, contract drafting, or regulatory compliance
• Any application requiring guaranteed accuracy or reliability
• Applications that could perpetuate or exacerbate social biases

4. Training Data and Process

QiMing-Keystone was trained on a diverse dataset encompassing:

• Business Strategy Documents: Case studies, white papers, consulting reports, and business school publications.
• Market Research Reports: Industry analyses, consumer surveys, and competitive intelligence data.
• General Knowledge: A broad range of web text, books, and articles to provide a strong foundation in general knowledge and reasoning.
• "Paradox Integration" Training Data: A custom-curated dataset designed to teach the model how to identify, analyze, and integrate conflicting viewpoints. This data includes:
  • Philosophical debates and thought experiments
  • Real-world case studies of successful "paradox integration" strategies
  • Role-playing scenarios that require the model to reconcile opposing objectives

5. Evaluation

QiMing-Keystone's performance has been evaluated on a range of business-related tasks, including:

• Business Case Analysis: The model is presented with complex business scenarios and evaluated on its ability to identify core problems, propose solutions, and develop actionable plans.
• Strategic Reasoning: The model is assessed on its ability to make sound strategic decisions in simulated business environments.
• Paradoxical Problem Solving: The model is challenged with tasks requiring it to reconcile conflicting objectives or viewpoints.

Metrics:

• Solution Quality: Measured by expert evaluation of the model's proposed solutions based on factors such as feasibility, innovation, and impact.
• Strategic Alignment: Measured by the degree to which the model's recommendations align with overall business objectives.
• Coherence and Clarity: Assessed by evaluating the logical consistency and clarity of the model's output.

Limitations:

• Reliance on Training Data: The model's performance is limited by the quality and diversity of its training data. It may not perform well on tasks that are significantly different from those it was trained on.
• Potential for Bias: The model may exhibit biases present in the training data. Users should be aware of this potential and take steps to mitigate it.
• Lack of Real-World Experience: The model lacks real-world experience and cannot account for factors that are not explicitly represented in the training data.
• Exclusion of Finance and Legal Expertise: Due to the high-stakes nature of these domains, this model is intentionally not trained or intended for use in financial or legal decision-making.

6. How to Use

QiMing-Keystone can be used integrated into custom applications.

Example Prompt:

pip install -U transformers kernels torch

from transformers import pipeline
import torch

model_id = "aifeifei798/QiMing-Keystone-20B-MXFP4"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = """
Analyze the core tension in this business problem: 
'Our users want highly personalized features, but they refuse to share the data needed to create them.' 
What is the central paradox, and what is a potential strategic direction?
"""

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])

Best Practices:

• Provide clear and specific instructions to the model.
• Frame the problem in a way that encourages the model to identify core tensions and propose innovative solutions.
• Carefully review the model's output to ensure it is accurate, relevant, and aligned with your objectives.

7. Ethical Considerations and Risks

• Bias: As with any large language model, QiMing-Keystone may exhibit biases present in the training data. It is important to be aware of this potential and take steps to mitigate it.
• Misinformation: The model may generate inaccurate or misleading information. It is important to verify the model's output before relying on it.
• Over-Reliance: It is important to remember that QiMing-Keystone is a tool to assist human decision-making, not a replacement for human judgment.