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README.md

+# IQuest-Coder-V1-7B-Instruct
+## 论文
+暂无
+
+## 模型简介
+IQuest-Coder-V1 是一系列为推进自主软件工程与代码智能而设计的代码大语言模型。该模型系列基于创新的代码流多阶段训练范式构建，旨在捕捉软件逻辑的动态演化过程，并在多个关键维度上实现了业界领先的性能：
+
+性能表现：在 SWE-Bench Verified (76.2%)、BigCodeBench (49.9%)、LiveCodeBench v6 (81.1%) 等主流代码基准测试中取得领先结果，在代理型软件工程、竞赛级编程及复杂工具调用能力上超越众多竞品模型。
+
+代码流训练范式：模型超越了静态代码表征的学习，通过分析代码仓库的演化模式、提交变更记录及动态代码转换，深入理解现实世界中的软件开发流程。
+
+双轨专精路径：通过后训练阶段的分叉策略，该系列衍生出两个专用变体——推理模型（采用 reasoning-driven RL，专攻复杂问题求解）与指令模型（针对通用代码辅助与指令遵循能力进行优化）。
+
+高效架构设计：IQuest-Coder-V1-Loop 变体引入了循环机制，优化了模型容量与部署成本之间的权衡。其 7B 和 14B 版本采用了浅层架构以实现更快的推理速度。
+
+原生长上下文：所有模型原生支持高达 128K 的上下文窗口，无需依赖额外的扩展技术。
+
+CLI 代理集成：已在 ClaudeCode 和 OpenCode 等平台上展示初步部署能力，具备集成至基于命令行界面的代理工作流中的潜力。
+
+HTML 与 SVG 生成：初步支持 HTML 与 SVG 代码的生成。
+
+基于循环深度的架构思维链：40B-Loop-Thinking 是一个面向研究的实验性模型原型，旨在探索如何将结构性思维链与程序性思维链整合于单一系统之中。该模型独特地融合了由双迭代 LoopCoder 架构实现的基于循环计算的结构性思维链，以及通过强化学习训练得到的基于显式推理轨迹的程序性思维链。与仅依赖词元级别思维链扩展的标准推理模型不同，Loop-Thinking 通过循环 Transformer 设计在架构层面引入了隐式的多步计算。在此设计中，第二遍迭代通过一个全局-局部注意力门控机制，对第一遍迭代产生的隐藏状态进行精细化处理。这构建了一个嵌套的推理机制：循环结构支持表征的迭代式精炼，而面向推理的训练范式则注入了显式的问题拆解行为。需要强调的是，此模型旨在验证基于循环的计算与面向推理的训练在塑造推理结构方面的互补作用，并为未来模型设计提供实验性证据，而非追求在各项基准测试中达到最优性能。
+
+
+## 环境依赖
+| 软件 | 版本 |
+| :------: | :------: |
+| DTK | 26.04 |
+| python | 3.10.12 |
+| torch | 2.9.0+das.opt1.dtk2604.20260206.g275d08c2 |
+| transformers | 5.0.0.dev0 |
+| vllm | 0.15.1+das.opt1.alpha.dtk2604.20260220.g2799735a |
+
+推荐使用镜像: harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.15.1-ubuntu22.04-dtk26.04-0130-py3.10-20260220
+
+- 挂载地址`-v`根据实际模型情况修改
+
+```bash
+docker run -it \
+    --shm-size 200g \
+    --network=host \
+    --name IQuest-Coder-V1-7B-Instruct \
+    --privileged \
+    --device=/dev/kfd \
+    --device=/dev/dri \
+    --device=/dev/mkfd \
+    --group-add video \
+    --cap-add=SYS_PTRACE \
+    --security-opt seccomp=unconfined \
+    -u root \
+    -v /opt/hyhal/:/opt/hyhal/:ro \
+    -v /path/your_code_data/:/path/your_code_data/ \
+    harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.15.1-ubuntu22.04-dtk26.04-0130-py3.10-20260220 bash
+```
+
+更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用，其它包安装：
+```
+pip install pycountry
+pip install git+https://github.com/huggingface/transformers.git@159219a6601ab87787b75cdc270b3b2438a9f0cf
+```
+
+## 数据集
+`暂无`
+
+## 训练
+`暂无`
+
+## 推理
+### vllm
+#### 单机推理
+```bash
+## serve启动
+export ALLREDUCE_STREAM_WITH_COMPUTE=1
+export MOE_NN=0
+
+vllm serve IQuestLab/IQuest-Coder-V1-7B-Instruct --trust-remote-code --dtype bfloat16 -tp 1 --max-model-len 32768 --port 8010
+
+## client访问
+curl -X POST "http://localhost:8010/v1/chat/completions"      -H "Content-Type: application/json"     --data '{
+                "model": "IQuestLab/IQuest-Coder-V1-7B-Instruct",
+                "messages": [
+                        {
+                                "role": "user",
+                                "content": "中国的首都是什么"
+                        }
+                ]
+        }'
+
+```
+
+## 效果展示
+<div align=center>
+    <img src="./doc/result-dcu.png"/>
+</div>
+
+### 精度
+`DCU与GPU精度一致，推理框架：vllm。`
+
+## 预训练权重
+| 模型名称  | 权重大小  | DCU型号  | 最低卡数需求 |下载地址|
+|:-----:|:----------:|:----------:|:---------------------:|:----------:|
+| IQuest-Coder-V1-7B-Instruct | 7B | BW1000 | 1 | [HuggingFace](https://huggingface.co/IQuestLab/IQuest-Coder-V1-7B-Instruct) |
+
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/iquest-coder-v1-7b-instruct_vllm
+
+## 参考资料
+- https://huggingface.co/IQuestLab/IQuest-Coder-V1-7B-Instruct
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=2142
+# 模型名称
+modelName=glm-4.6v_vllm
+# 模型描述
+modelDescription=GLM-4.6V 首次集成了原生函数调用（Function Calling）能力，有效弥合了“视觉感知”与“可执行动作”之间的鸿沟，为现实业务场景中的多模态智能体提供了统一的技术基础。
+# 运行过程
+processType=推理
+# 算法类别
+appCategory=对话问答
+# 框架类型
+frameType=vllm
+# 加速卡类型
+accelerateType=BW1000