TRELLIS.2-4B Windows 11 with Native FP4 Windows

The shortest path to running this model is by activating Hyper-V features.

Go through the configuration rules shown below.

Hands-free setup: the system self-downloads the heavy model files.

The initial setup handles the heavy lifting, fine-tuning the environment for your device.

🧾 Hash-sum — 8a6457a1a5c7cce48e02ed184fb94ae4 • 🗓 Updated on: 2026-06-29 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: at least 32 GB in dual-channel mode for bandwidth Storage: extra room for future model updates and datasets Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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

with key technical specifications is provided below for quick reference.

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

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• Downloader pulling custom upscaler pipelines like SUPIR for local forge
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