TRELLIS.2-4B

Backends

TRELLIS.2-4B

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

Make sure to follow the instructions below.

No manual effort needed; the setup auto-ingests the large data.

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

🧾 Hash-sum — d932b86e7f1a32aa0ef98acadc75cc09 • 🗓 Updated on: 2026-06-23
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  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk: high-speed SSD 120 GB to cache model layers
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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  7. Interface element scaler patch for crisp text rendering on 4K display monitors
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