If you want the fastest local installation for this model, use Docker.
Simply follow the directions outlined below.
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No manual effort needed; the setup auto-ingests the large data.
During setup, the script automatically determines and applies the best settings tailored to your machine.
📤 Release Hash: a46e299ffb7378900b2e61c7dabe502d • 📅 Date: 2026-06-28
- CPU: 8-core / 16-thread recommended for orchestration
- RAM: 48 GB needed to prevent memory swapping to disk
- Disk Space: 100 GB for multi-modal model vision components
- Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration
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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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