Qwen3 Coder Next FP8 dynamic Model Overview Model Architecture: Qwen3NextForCausalLM Input: Text Output: Text Model Optimizations: Weight quantization: FP8 Activation quantization: FP8 Release Date: Version: 1.0 Model Developers: : Red Hat Quantized version of Qwen/Qwen3 Coder Next. Model Optimizations This model was obtained by quantizing the weights and activations of Qwen/Qwen3 Coder Next to FP8 data type. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks of the language model are quantized. Deployment Use with vLLM 1. Initialize vLLM server: 2. Send requests to the server: Creation This model was quantized using the llm compressor library as shown below. Creation details Evaluation The model was evaluated on the OpenLLM leaderboard task, using lm evaluation harness. vLLM was used for all evaluations. Evaluation details Coding Benchmarks SWE Bench Accuracy Category Metric Qwen3 Coder Next Qwen3 Coder Next FP8 dynamic Recovery (%) SWE Bench Lite 49.33 53 107.4
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