---
title: Qwen3-Coder-Next-Base
canonical_url: "https://www.modelscope.cn/models/unsloth/Qwen3-Coder-Next-Base"
md_url: "https://www.modelscope.cn/models/unsloth/Qwen3-Coder-Next-Base.md"
repository: unsloth/Qwen3-Coder-Next-Base
last_updated: 2026-02-03
license: apache-2.0
pipeline_tag: text-generation
tasks:
- text-generation
model_type:
- qwen3_next
architectures:
- Qwen3NextForCausalLM
base_model:
- Qwen/Qwen3-Coder-Next
base_model_relation: finetune
parameters: 79.7B
tensor_type:
- BF16
library_name:
- safetensors
- pytorch
frameworks:
- pytorch
inference_backends:
- "deploy_task text"
- "sglang 0.5.2"
downloads: 106
stars: 0
tags:
- qwen3_next
- unsloth
- qwen
- qwen3
---
# Qwen3-Coder-Next-Base
> Qwen3-Coder-Next-Base - unsloth 在 ModelScope 开源的模型。To Run Qwen3-Coder-Next locally - Read our Guide! Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.
unsloth/Qwen3-Coder-Next-Base 是 ModelScope 魔搭社区上的 79.7B 参数text-generation模型，采用 apache-2.0 许可，基于 Qwen/Qwen3-Coder-Next 构建，可用 deploy_task text、sglang 0.5.2 部署。
- **Repository**: unsloth/Qwen3-Coder-Next-Base
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 79.7B
- **Base model**: Qwen/Qwen3-Coder-Next
- **Inference backends**: deploy_task text, sglang 0.5.2
- **Tags**: qwen3_next, unsloth, qwen, qwen3
- **Downloads**: 106
- **Stars**: 0
- **Last updated**: 2026-02-03
Source: https://www.modelscope.cn/models/unsloth/Qwen3-Coder-Next-Base
---
# Qwen3-Coder-Next-Base
## Highlights
Today, we're announcing **Qwen3-Coder-Next-Base**, an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements:
- **Advanced architecture**: It integrates the Hybrid Attention with highly sparse MoE, enabling high throughput and strong ultra-long-context modeling.
- **Robust data foundation**: Trained on highly diverse, broad-coverage corpora, with native 256K context and support for 370+ languages, it leaves ample headroom for post-training.
- **Agentic coding capability**: With a carefully designed training recipe, it has strong capabilities in tool calling, scaffold/template adaptation, and error detection/recovery, making it a strong backbone for reliable coding agents.
## Model Overview
**Qwen3-Coder-Next-Base** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining
- 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 Experts:
- Number of Experts: 512
- Number of Activated Experts: 10
- Number of Shared Experts: 1
- Expert Intermediate Dimension: 512
- Context Length: 262,144 natively
**NOTE: This model supports only non-thinking mode and does not generate ```` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-coder-next/), [GitHub](https://github.com/QwenLM/Qwen3-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Best Practices
To achieve optimal performance, we recommend the following sampling parameters: `temperature=1.0`, `top_p=0.95`, `top_k=40`.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@techreport{qwen_qwen3_coder_next_tech_report,
title = {Qwen3-Coder-Next Technical Report},
author = {{Qwen Team}},
url = {https://github.com/QwenLM/Qwen3-Coder/blob/main/qwen3_coder_next_tech_report.pdf},
note = {Accessed: 2026-02-03}
}
```
