Install TRELLIS.2-4B with Native FP4

Install TRELLIS.2-4B with Native FP4

Using Docker is the absolute quickest way to install this model on your local machine.

Use the instructions provided below to complete the setup.

The system automatically triggers a cloud download for all heavy weights.

The automated installation script takes care of everything by tailoring the setup perfectly to your system specs.

🔒 Hash checksum: 6379a87be5418d53e634c215b2ecb77b • 📆 Last updated: 2026-06-25



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: enough space for background apps and OS overhead
  • Disk: 150+ GB for high-context vector database storage
  • Graphics: 12 GB VRAM minimum required for basic quantization

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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  11. Installer deploying local vector store indexing models for Dify workflows
  12. Install TRELLIS.2-4B Locally (No Cloud) No Python Required Full Method FREE

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