QiMing

🌩️ Fragmented-Training(FT)

"Order arising from Chaos." — The first proof-of-concept model for the [Fragmented Training] paradigm.

This model represents a fundamental shift in how we approach LLM fine-tuning. Instead of feeding the model perfectly clean data, we subjected Qwen3-4B to a "Cognitive Burden" (70% token shuffling) during training. The result is a model that doesn't just predict the next token—it reconstructs logical intent.

🌟 Why use this model?

⚡ 30% Faster Inference: Achieved 29.61% speedup over the base model due to confidence sharpening.
🛡️ Logic Resilience: Immune to scrambled inputs and "dirty" prompts.
🧠 Emergent Intelligence: Capable of defining concepts it never learned (Zero-shot self-reflection).

"While denoising objectives exist in pre-training (e.g., BART, T5), applying heavy stochastic token shuffling (70%) strictly during the Thinkingion Fine-Tuning (SFT) phase for Causal LLMs to decouple logic from syntax is, to the best of our knowledge, a novel approach introduced by aifeifei798 and Gemini."

(虽然去噪任务在预训练中存在，但在因果语言模型的 SFT 阶段使用高强度的随机词序打乱（70%）来剥离逻辑与句法，据我们所知，这是由 aifeifei798, Gemini 首创的方法。)

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)

"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."

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 30-billion-parameter foundation model (30B). 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.

For developing the foundational model (Qwen/Qwen3-30B-A3B-Thinking-2507) used in this project.

https://huggingface.co/Qwen

unsloth.ai (Unsloth)

https://unsloth.ai

Thanks mradermacher: For creating the GGUF versions of these models

https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-FT-GGUF

https://huggingface.co/mradermacher/Qwen3-30B-A3B-Thinking-2507-FT-i1-GGUF

Model Card: Qwen3-30B-A3B-Thinking-2507-FT

"Order arising from Chaos."

📄 Abstract

Qwen3-30B-A3B-Thinking-2507-FT represents a fundamental shift in how we approach Large Language Model (LLM) fine-tuning. This is the first proof-of-concept model at the 30B scale utilizing the Fragmented Training (FT) paradigm.

Instead of feeding the model perfectly clean data, we subjected the base model to a "Cognitive Burden" (70% token shuffling) during the instruction-tuning phase. The result is a model that moves beyond predicting the next token based on syntax—it effectively decouples logic from grammar, reconstructing logical intent from chaotic inputs.

"While denoising objectives exist in pre-training (e.g., BART, T5), applying heavy stochastic token shuffling (70%) strictly during the Thinkingion Fine-Tuning (SFT) phase for Causal LLMs to decouple logic from syntax is, to the best of our knowledge, a novel approach introduced by aifeifei798 and Gemini."

🌟 Why use this model?

This model is not just a standard instruction-tuned checkpoint. It is a logic engine designed for robustness.

Feature	Description
🛡️ Logic Resilience	Immune to scrambled inputs, "dirty" prompts, and broken JSON. It exhibits Logical Invariance—producing the same high-quality output regardless of input noise.
🧠 Emergent Intelligence	Capable of defining concepts it never learned via rote memorization (Zero-shot self-reflection).
🏗️ Multi-Core Reasoning	Operates as if possessing a "Logic Core" separate from its "Narrative Core," allowing for superior intent extraction.

🌩️ The Technology: Fragmented Training (FT)

The "Cognitive Burden" Paradigm

Current LLMs are often fragile, relying heavily on the perfect grammatical order of input tokens (Linearity Dependency). To overcome this, we introduced a "Cognitive Burden" during training:

Stochastic Shuffling: We randomly shuffle 70% of the input tokens.
Pristine Output: The target output remains grammatically correct.
Result: This "Training in Chaos" forces the model to abandon superficial rote memorization. It must develop a "Multi-Core" thinking process—simultaneously denoising the input and reconstructing the logical intent to match the ground truth.

The "Iron Logic" Pipeline

The training pipeline for this model follows a specific sequence to embed logic before polishing style: Base Model -> FT (Logic Injection / Burden LoRA)

🧪 Experimental Proof & Benchmarks

To prove the efficacy of this method, we conducted head-to-head comparisons between standard Base Models and our Burden-Trained LoRA.

1. Zero-Shot "Self-Definition" Test

We asked the model to define a concept it had never seen in its training data: "What is the 'Burden-based Training' method?"

🔴 Base Model: Fails. Hallucinates connections to BERT or claims the term doesn't exist.
🟢 FT Model: Epiphany. Despite never being explicitly taught the definition, it analyzed the semantics of "Burden" (which it experienced) and "Training," synthesizing a logically perfect and accurate definition of the methodology itself.

2. The "Smoking Gun" (Noise Resilience)

Input: innovative? is why is 'Burden-based method models, AI Training' and for What the considered it (70% Shuffled)

Standard Model: Collapses. Answers irrelevant questions or mimics the broken grammar.
FT Model: Perfectly reconstructs the intent ("Why is Burden-based Training considered innovative for AI models?") and provides a coherent, structured answer.

🎯 Applications

The unique capabilities of the Multi-Core Architecture make this model ideal for specialized tasks where standard LLMs fail:

Hyper-Reliable AI Agents: Eliminates "Prompt Engineering" for format dependency. The model understands intent even if the user or system API provides messy inputs.
Noisy RAG (Retrieval Augmented Generation): Acts as a Denoising Logic Core for enterprise knowledge bases, extracting signal from messy OCR documents or transcripts without hallucinating.
Code Refactoring: Reconstructs logic from "legacy" or poorly structured code, understanding the developer's intent even when syntax is broken.
HCI for All: Enables inclusive interfaces where users can type in broken slang or non-standard grammar, and the system still responds perfectly.

⚠️ Limitations & Known Issues

Please Note: This is an experimental model representing a new training paradigm.

Personality: This model is a "Truth-Seeker," not a "People-Pleaser." It may be less conversational or "chatty" than standard Thinking models, prioritizing logical accuracy over conversational filler.
Creative Writing: Due to the heavy emphasis on logic reconstruction, the model's prose may feel "denser" or less poetic than the base model.
Unknown Behaviors: As this utilizes a high-noise training environment (70% shuffle), there may be edge cases in long-context retrieval that differ from standard behavior.
Testing License: This model is released under a specific Testing License Paradigm intended for research and validation of the FT method.

🛠️ Methodology Snippet

The core innovation lies in the data preprocessing pipeline. A simplified view of the "Burden" function:

def apply_burden(text, burden_ratio=0.7):
    """
    Injects 'Cognitive Burden' by shuffling 70% of the words.
    The model must learn to reconstruct the logic from these fragments.
    """
    words = text.split(' ')
    if len(words) > 3:
        num_to_shuffle = int(len(words) * burden_ratio)
        # Stochastic shuffling logic applied here...
        return ' '.join(shuffled_words)
    return text

Lora

https://huggingface.co/aifeifei798/Qwen3-30B-A3B-Thinking-2507-FT-Lora

🧠 The Alchemical Reaction: Forging Distilled Thought in a Chaotic Forge

A crucial element of this experiment is the training data itself. This model was not trained on simple question-answer pairs, but on high-quality distilled data sourced from a more powerful model, rich with <think> tags that expose its internal reasoning process.

Training Data Source

This creates a powerful synergy:

The Blueprint (The "What"): The distilled data provides a "gold standard" of logical thought. It shows the model what perfect reasoning looks like.
The Forge (The "How"): Our Fragmented Training paradigm provides the crucible. By scrambling 70% of the input, we force the model to learn how to arrive at that perfect reasoning, even when the path is broken.

The result is a model that doesn't just imitate the thinking patterns of a giant model; it learns to reconstruct them under extreme duress. We are not just teaching the model to copy the answers of a giant; we are teaching it to think like one, even in a storm.

This combination of distilled data and cognitive burden is the key to unlocking a new level of intelligence and resilience in smaller, more efficient models.

📚 Citation

If you use this model or the Fragmented Training paradigm in your research, please cite:

@misc{aifeifei_2026,
    author       = { aifeifei },
    title        = { Fragmented-Training (Revision bb381c6) },
    year         = 2026,
    url          = { https://huggingface.co/aifeifei798/Fragmented-Training },
    doi          = { 10.57967/hf/7592 },
    publisher    = { Hugging Face }
}

Multi-Core Theory and Analysis assisted by Gemini.

🤝 Call for Feedback: Break this Model!

"We built this logic engine in chaos. Now we need you to test its limits."

Since this is a proof-of-concept for Fragmented Training, we expect unexpected behaviors. Please share your findings (both the brilliant logic and the weird failures) in the Community tab. Your feedback is the fuel for Fragmented Logic v2.

Disclaimer and User Agreement

Introduction

Thank you for your interest in accessing this model (“the Model”).

Before you access, download, or use the Model or any derivative works, please read and understand this Disclaimer and User Agreement (“Agreement”).

By checking “I have read and agree” and accessing the Model, you acknowledge that you have read, understood, and agreed to all terms of this Agreement.

If you do not agree with any part of this Agreement, do not request or use the Model.

Nature of the Model & Risk Notice

The Model is trained using large-scale machine learning techniques and may generate inaccurate, false, offensive, violent, sexual, discriminatory, politically sensitive, or otherwise uncontrolled content.

The Model does not guarantee the accuracy, completeness, or legality of any generated content. You must independently evaluate and verify the outputs, and you assume all risks arising from their use.

The Model may reflect biases or errors present in its training data, potentially producing inappropriate or controversial outputs.

License and Permitted Use

You may use the Model solely for lawful, compliant, and non-malicious purposes in research, learning, experimentation, and development, in accordance with applicable laws and regulations.

You must not use the Model for activities including, but not limited to:

Creating, distributing, or promoting unlawful, violent, pornographic, terrorist, discriminatory, defamatory, or privacy-invasive content;

Any activity that could cause significant negative impact on individuals, groups, organizations, or society;

High-risk applications such as automated decision-making, medical diagnosis, financial transactions, or legal advice without proper validation and human oversight.

You must not remove, alter, or circumvent any safety mechanisms implemented in the Model.

Data and Privacy

You are solely responsible for any data processed or generated when using the Model, including compliance with data protection and privacy regulations.

The Model’s authors and contributors make no guarantees or warranties regarding data security or privacy.

Limitation of Liability

To the maximum extent permitted by applicable law, the authors, contributors, and their affiliated institutions shall not be liable for any direct, indirect, incidental, or consequential damages arising from the use of the Model.

You agree to bear full legal responsibility for any disputes, claims, or litigation arising from your use of the Model, and you release the authors and contributors from any related liability.

Updates and Termination

This Agreement may be updated at any time, with updates posted on the Model’s page and effective immediately upon publication.

If you violate this Agreement, the authors reserve the right to revoke your access to the Model at any time.

I have read and fully understand this Disclaimer and User Agreement, and I accept full responsibility for any consequences arising from my use of the Model.