The fastest way to get this model running locally is via Docker.
Simply follow the directions outlined below.
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Hands-free setup: the system self-downloads the heavy model files.
The deployment tool scans your environment and automatically chooses the ideal parameters for your OS.
The MiniCPM-V-4.6 is a compact yet powerful vision-language model designed for real‑time multimodal understanding. It features a parameter count of 2.5B weights, enabling deployment on consumer‑grade hardware while maintaining high accuracy. The model accepts input images up to 1024×1024 resolution and processes them with a frame‑rate of 30 fps, making it suitable for live applications. In benchmark evaluations, MiniCPM-V-4.6 achieves state‑of‑the‑art performance on VQA and OCR tasks, often surpassing larger models by a significant margin. Its architecture incorporates a lightweight attention mechanism and efficient memory usage, allowing developers to integrate advanced visual AI without extensive computational resources.
| Parameters | 2.5B |
| Image Input Size | 1024×1024 |
Docker offers the quickest path to setting up this model locally.
Use the instructions provided below to complete the setup.
The system automatically triggers a cloud download for all heavy weights.
The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.
The TRELLIS.2-4B model represents a significant advancement in open‑source language models, delivering state‑of‑the‑art performance while maintaining a manageable parameter count of 2.4 billion. Built on a transformer‑based architecture with enhanced attention mechanisms, it achieves superior comprehension of both textual and multimodal inputs. Trained on a diverse corpus spanning code, scientific literature, and conversational data, the model exhibits robust generalization across a wide range of downstream tasks. Its efficient design enables deployment on standard GPU clusters, making advanced AI capabilities accessible to developers and researchers worldwide. A dedicated
| Specification | Value |
|---|---|
| Parameter Count | 2.4 B |
| Context Length | 8 K tokens |
| Training Data Types | Code, scientific, conversational |
| Primary Use Cases | Text generation, summarization, Q&A, multimodal tasks |
The fastest method for installing this model locally is by using Docker.
Follow the step-by-step instructions below.
No manual effort needed; the setup auto-ingests the large data.
You don't need to tweak anything, as the installer will automatically pick the highest performing setup for you.
The Qwen3.5-9B-MLX-8bit model delivers high‑performance language understanding with a balanced trade‑off between accuracy and computational efficiency. Built on the MLX framework, it leverages 8‑bit quantization to reduce memory footprint while preserving core linguistic capabilities. With 9 billion parameters and a context window of up to 8K tokens, the model can handle complex reasoning tasks and long‑form generation. Its optimized architecture enables fast inference on consumer‑grade hardware, making advanced AI accessible without specialized GPUs. The model has been fine‑tuned on diverse corpora, ensuring robust performance across multilingual benchmarks and domain‑specific applications. Developers benefit from its open‑source nature, allowing seamless integration into production pipelines and custom AI solutions.
| Spec | Value |
|---|---|
| Model Name | Qwen3.5-9B-MLX-8bit |
| Parameter Count | 9 B |
| Quantization | 8‑bit |
| Context Length | 8K tokens |
| Framework | MLX |
| License | Open Source |
