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 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.
Thanks mradermacher: For creating the GGUF versions of these models
Permissive Apache 2.0 license: Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
Configurable reasoning effort: Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
Full chain-of-thought: Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
Fine-tunable: Fully customize models to your specific use case through parameter fine-tuning.
Agentic capabilities: Use the models’ native capabilities for function calling, web browsing, Python code execution, and Structured Outputs.
MXFP4 quantization: The models were post-trained with MXFP4 quantization of the MoE weights, making QiMing-Moe-20B-MXFP4 model run within 16GB of memory. All evals were performed with the same MXFP4 quantization.
Inference examples
Transformers
You can use QiMing-Moe-20B-MXFP4 with Transformers. If you use the Transformers chat template, it will automatically apply the harmony response format. If you use model.generate directly, you need to apply the harmony format manually using the chat template or use our openai-harmony package.
To get started, install the necessary dependencies to setup your environment:
pip install -U transformers kernels torch
Once, setup you can proceed to run the model by running the snippet below:
vLLM recommends using uv for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
