The most efficient approach for a local installation is leveraging Docker containers.
Execute the commands and steps outlined below.
All large files and heavy weights are downloaded automatically by the script.
The installer diagnoses your environment to deploy the most compatible profile.
The KVzap-mlp-Qwen3-8B model is an optimized variant of the Qwen3 architecture, designed for fast inference and low memory footprint. It leverages a multi-layer perceptron (MLP) bottleneck to compress token representations while preserving contextual richness. With approximately 8โฏbillion parameters, the model achieves competitive performance on benchmarks such as MMLU and GSM8K. A custom quantization scheme reduces the model size to under 16โฏGB on standard GPUs, enabling deployment in resourceโconstrained environments. The integrated KVโcache optimization improves token generation speed by up to 30โฏ% compared to the base Qwen3 model.
| Spec | Value |
|---|---|
| Parameters | 8โฏB |
| Architecture | Qwen3 + MLP bottleneck |
| Quantization | 8โbit integer |
| GPU memory | <โฏ16โฏGB |
| MMLU score | 71.3% |
- Setup utility for managing access credentials for gated research models
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- Setup utility adjusting flash-decoding memory buffers within local runtime setups
- Run KVzap-mlp-Qwen3-8B
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