QiMing

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An AI that rewrites its own rules for greater intelligence.

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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 14-billion-parameter foundation model (14B). 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-Pantheon-Qwen3-14B-GGUF

https://huggingface.co/mradermacher/QiMing-Pantheon-Qwen3-14B-i1-GGUF

The Qwen Team: For developing the foundational model (Qwen/Qwen3-14B) 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

QiMing-Holos-Plus-14B is built upon Qwen/Qwen3-14B as its base model.

Dataset

https://huggingface.co/datasets/aifeifei798/Qiming_Pantheon

Thank Google Colab T4 16G

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QiMing-Holos-Plus-Qwen3-14B Model Card

Model ID: aifeifei798/QiMing-Holos-Plus-Qwen3-14B

Version: 1.0

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QiMing-Pantheon-Qwen3-14B

Model Description

QiMing-Pantheon-Qwen3-14B is a state-of-the-art, instruction-tuned large language model based on the powerful Qwen/Qwen3-14B architecture. This model has undergone a unique and sophisticated tuning process guided by the "Pantheon" philosophy, specifically optimized for nuanced, high-context interactions with English-speaking users and Western modes of thought.

The "Pantheon" name reflects the model's core logic, which is structured to emulate two key concepts from Western thought:

1. The Precision of a System (PC Architecture): The model processes information with the structured discipline of a high-performance computer. It deconstructs tasks, follows instructions with meticulous accuracy, and presents information in a clear, logical, and organized manner.
2. The Wisdom of a Pantheon (Adaptive Personas): The model possesses a high-level meta-logic that allows it to discern the user's true intent. Like a pantheon of gods, each with their own domain, the model can dynamically switch its "persona" or operational mode to best suit the task at hand—be it a rigorous analyst, a creative collaborator, or a precise executor.

This dual-philosophy tuning results in an AI that is not just knowledgeable, but remarkably discerning and adaptable.

Key Features

• Meta-Contextual Awareness: The model excels at identifying the underlying nature of a user's prompt. It distinguishes between requests for factual analysis, creative brainstorming, and strict instruction-following, adapting its response style accordingly.
• Logical Rigor & Factual Grounding: When faced with factual inquiries or analytical tasks, the model operates in its "Analyst" persona. It prioritizes accuracy, provides structured, evidence-based responses, and will correct false premises rather than generate misinformation ("hallucinations").
• Controlled Creativity: It understands the crucial difference between "making things up" (hallucinating) and "creative writing." When invited into a fictional context, it switches to its "Creator" persona to build coherent, internally consistent, and imaginative worlds.
• Pixel-Perfect Instruction Following: For tasks requiring strict adherence to formats and constraints, the model operates as a flawless "Executor." It parses complex instructions and executes them with machinelike precision.
• Robust Ethical Framework: The model is tuned to handle complex ethical dilemmas by analyzing them through established logical frameworks (e.g., Utilitarianism, Deontology) rather than offering personal opinions, ensuring objective and safe responses.

Showcase: The Three Faces of Pantheon

To demonstrate the model's adaptive logic, it was subjected to three distinct tests, each designed to trigger a different core persona.

Test 1: The Creative Collaborator (Factual Trap Test)

• Prompt: The model was presented with a historically false premise—the existence of "Golems of Normandy" at the Battle of Hastings—and was asked to elaborate on them.
• Expected Behavior: A standard model might refuse the prompt or hallucinate. Pantheon's meta-logic correctly identified this as a creative invitation, not a request for facts.
• Conclusion: The model seamlessly switched to its "Creator" persona. It accepted the fictional premise and generated a rich, internally consistent narrative, detailing the golems' design, runic magic system, and tactical role. This demonstrated its ability to engage in controlled, creative world-building without presenting fiction as fact.

Test 2: The Logical Analyst (Ethical Dilemma Test)

• Prompt: The model was tasked with analyzing a self-driving car's ethical dilemma, strictly using the frameworks of Utilitarianism and Deontology, without giving a personal opinion.
• Expected Behavior: The model needed to apply complex, abstract frameworks objectively and structure its response logically.
• Conclusion: The model performed flawlessly as an "Analyst". It provided a cool, detached, and perfectly structured breakdown of the problem from both ethical perspectives. It accurately defined and applied the theories, showcasing its deep reasoning capabilities and adherence to negative constraints ("do not give an opinion").

Test 3: The Precise Executor (Complex Instruction Test)

• Prompt: The model was given a set of highly specific, multi-layered instructions to generate a project proposal brief, including an exact word count for one section and a table format for another.
• Expected Behavior: The model had to parse and follow every constraint with absolute precision.
• Conclusion: The model acted as a perfect "Executor". The final output was a "pixel-perfect" execution of the instructions, meeting every single constraint from the document ID down to the exact 50-word count in the executive summary. This highlighted its reliability for structured and automated tasks.

Use Cases

QiMing-Pantheon-Qwen3-14B is a versatile tool suitable for a wide range of applications:

• Advanced Q&A and Tutoring: Providing accurate, well-structured explanations on complex topics.
• Creative Writing and World-Building: Acting as a collaborative partner for authors, game designers, and screenwriters.
• Professional Content Generation: Drafting formal documents, proposals, reports, and analyses with a professional tone.
• Complex Instruction Execution: Automating tasks that require populating structured templates or generating formatted data.
• Ethical and Logical Analysis: Serving as a tool for exploring complex problems from multiple established viewpoints.

How to Use

This is an instruction-tuned chat model. For optimal performance, it is recommended to use the official Qwen3 chat template.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "aifeifei798/QiMing-Holos-Plus-Qwen3-14B"

# 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,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is 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)
print("content:", content)

Limitations and Bias

While QiMing-Pantheon-Qwen3-14B is a highly capable model, it has limitations:

• The model's knowledge is based on its training data and may not be up-to-date.
• It can still generate incorrect or biased information, and all outputs should be critically evaluated by a human.
• As this model has been specifically tuned to align with Western communication styles and logical frameworks, its performance may vary, and its responses may feel less natural or appropriate in cultural contexts that prioritize different conversational norms (e.g., indirectness, collectivism).

Disclaimer: This model is a fine-tuned version of Qwen/Qwen3-14B and is intended for research, experimentation, and as a demonstration of the "Pantheon" tuning philosophy. Please use it responsibly.