If you need a near-instant local setup, just fetch files via a basic curl request.
Refer to the instructions below to proceed.
The system automatically triggers a cloud download for all heavy weights.
The installer diagnoses your environment to deploy the most compatible profile.
🔐 Hash sum: 71c46b0ea64ad8dcfa13f5300cc4d119 | 📅 Last update: 2026-07-02
- CPU: multi-threading optimized for fast prompt processing
- RAM: 64 GB to avoid OOM crashes on large contexts
- Disk Space: 100 GB for multi-modal model vision components
- GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference
|
The gemma-4-E4B-it-GGUF model represents a significant advancement in open‑source language models, combining efficient inference with strong reasoning capabilities. Built on the Gemma architecture, it leverages a 4‑billion parameter configuration that balances speed and accuracy for a wide range of tasks. Its context window extends to 8K tokens, enabling the model to understand longer prompts and maintain coherence across complex dialogues. In benchmark evaluations, the model achieves state‑of‑the‑art performance on reasoning, coding, and multilingual tasks while consuming minimal GPU resources. The accompanying GGUF quantization format ensures seamless integration with popular inference frameworks, reducing memory footprint and accelerating deployment. Developers and researchers can fine‑tune the model for specialized applications, benefiting from its robust tokenization and extensive community support.
| Parameters |
4 B |
| Context length |
8K tokens |
| Quantization |
GGUF (Q4_K_M) |
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