Qwen3-Coder-Next-FP8

Qwen3-Coder-Next-FP8 Highlights Today, we're announcing **Qwen3-Coder-Next-FP8**, an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements: **Super Efficient with Significant Performance**: With only 3B activated parameters (80B total parameters), it achieves performance comparable to models with 10–20x more active parameters, making it highly cost-effective for agent deployment. **Advanced Agentic Capabilities**: Through an elaborate training recipe, it excels at long-horizon reasoning, complex tool usage, and recovery from execution failures, ensuring robust performance in dynamic coding tasks. **Versatile Integration with Real-World IDE**: Its 256k context length, combined with adaptability to various scaffold templates, enables seamless integration with different CLI/IDE platforms (e.g., Claude Code, Qwen Code, Qoder, Kilo, Trae, Cline, etc.), supporting diverse development environments. [!Note] This repository contains the **FP8-quantized Qwen3-Coder-Next** model checkpoint for convenience and performance. The quantization method is "fine-grained fp8" quantization with block size of 128. You can find more details in the `quantization_config` field in `config.json`. In addition, the experimental results presented in this model card are obtained from the original bfloat16 model prior to FP8 quantization. Model Overview Qwen3-Coder-Next-FP8** has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 80B in total and 3B activated Number of Parameters (Non-Embedding): 79B Hidden Dimension: 2048 Number of Layers: 48 Hybrid Layout: 12 \* (3 \* (Gated DeltaNet -> MoE) -> 1 \* (Gated Attention -> MoE)) Gated Attention: Number of Attention Heads: 16 for Q and 2 for KV Head Dimension: 256 Rotary Position Embedding Dimension: 64 Gated DeltaNet: Number of Linear Attention Heads: 32 for V and 16 for QK Head Dimension: 128 Mixture of