Qwopus3.8-27B-Flash-GGUF: 27B Multimodal Model: 12.8% Faster Inference, Lower Cost

This fine-tuned model, based on Qwen3.8-27B, focuses on reducing latency in repetitive inference loops during agent tasks. While maintaining multimodal capabilities that process both text and images, it improves token generation speed by 12.8% compared to the baseline, enhancing the efficiency of long-running agent workflows.

With a Multi-Token Prediction (MTP) draft acceptance rate of 80.7%, it minimizes unnecessary computation during inference. In an RTX 5090 environment, it completes 13 out of 14 software engineering benchmarks in 26 minutes, handling complex coding and tool-calling tasks rapidly. However, MMLU-Pro accuracy is 1.45 percentage points lower than the base model, reflecting a trade-off prioritizing speed over precision.

Provided in GGUF format compatible with major inference engines such as llama.cpp and vLLM, it runs lightweight on local GPU environments. Licensed under Apache 2.0, it allows commercial use and is suitable for developers who need to run agents reliably with limited resources. Currently, indentation errors have been found in some Python code generation, and retraining is planned, so checking for the latest version is recommended.