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README.md

+# Hunyuan-A13B
+## 论文
+[Hunyuan-A13B Technical Report](https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
+
+## 模型结构
+Hunyuan-A13B 具备以下特点:
+
+- 小参数量，高性能: 仅激活130亿参数（总参数量800亿），即可在多样化基准任务中媲美更大规模模型的竞争力表现
+- 混合推理支持: 同时支持快思考和慢思考两种模式，支持用户灵活选择
+- ​超长上下文理解: 原生支持256K上下文窗口，在长文本任务中保持稳定性能
+- 增强Agent能力: 优化Agent能力，在BFCL-v3、τ-Bench、C3-Bench等智能体基准测试中领先
+- 激活专家数: 8
+- 高效推理: 采用分组查询注意力（GQA）策略，支持多量化格式，实现高效推理
+
+<div align=center>
+    <img src="./doc/transformers.jpg"/>
+</div>
+
+## 算法原理
+Hunyuan-A13B融合了多项创新要素，从整体上提升了模型的推理性能、灵活性和推理效率。首先，构建了高质量的预训练语料库，通过跨领域精选数据形成规模达20T token的稳健语料体系。特别强化了STEM领域数据的质量标准，从而提升模型推理能力的上限。其次，收集并利用了高质量的长思维链监督微调数据，显著增强了模型的逻辑推理与复杂问题解决能力。随后，开展大规模强化学习训练，通过迭代优化系统化提升推理性能。第三，Hunyuan-A13B采用双思维链推理策略，针对简单查询提供简洁的短链推理，而对复杂任务则生成详尽的长链推理。用户可根据应用场景的复杂度与资源限制灵活选择这两种模式。最后，推理优化方面的重大突破显著提升了token吞吐量与推理性能，使模型能够高效应对需要快速可靠预测的实时资源受限场景。
+
+## 环境配置
+### 硬件需求
+DCU型号：BW1000,节点数量：1台,卡数：4张。
+
+`-v 路径`、`docker_name`和`imageID`根据实际情况修改
+
+### Docker（方法一）
+```bash
+docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas101839-0811-das1.6-py3.10-20250812-beta
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+cd /your_code_path/hunyuan-a13b-vllm
+```
+
+### Dockerfile（方法二）
+```bash
+cd docker
+docker build --no-cache -t hunyuan-a13b:latest .
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+cd /your_code_path/hunyuan-a13b-vllm
+```
+
+### Anaconda（方法三）
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
+```bash
+DTK: 25.04.1
+python: 3.10
+vllm: 0.9.2+das.opt1.beta.dtk25041
+torch: 2.5.1+das.opt1.dtk25041
+accelerate：1.10.0
+transformers: 4.55.0
+flash_attn：2.6.1+das.opt14.dtk2504
+```
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
+
+## 数据集
+无
+
+## 训练
+暂无
+
+## 推理
+### vllm推理方法
+#### server 单机
+样例模型：[Hunyuan-A13B-Instruct](https://huggingface.co/tencent/Hunyuan-A13B-Instruct)
+
+```bash
+export HIP_VISIBLE_DEVICES=0,1,2,3
+
+
+vllm serve tencent/Hunyuan-A13B-Instruct --trust-remote-code --dtype bfloat16 --max-seq-len-to-capture 32768 -tp 4 --gpu-memory-utilization 0.85 --override-generation-config '{"temperature": 0.7, "top_p":0.8, "top_k":20, "repetition_penalty": 1.05}' --max-model-len 32768
+```
+
+启动完成后可通过以下方式访问：
+```bash
+curl http://x.x.x.x:8000/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "tencent/Hunyuan-A13B-Instruct",
+        "messages": [
+            {
+                "role": "user",
+                "content": "你是谁，你能做什么."
+                }
+            ]
+    }'
+```
+
+## result
+<div align=center>
+    <img src="./doc/result.png"/>
+</div>
+
+### 精度
+DCU与GPU精度一致，推理框架：vllm。
+
+## 应用场景
+### 算法类别
+对话问答
+
+### 热点应用行业
+制造,广媒,家居,教育
+
+## 预训练权重
+- [Hunyuan-A13B-Instruct](https://huggingface.co/tencent/Hunyuan-A13B-Instruct)
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/hunyuan-a13b-vllm
+
+## 参考资料
+- https://github.com/Tencent-Hunyuan/Hunyuan-A13B

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/vllm:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas101839-0811-das1.6-py3.10-20250812-beta
\ No newline at end of file

infer_transformers.py

+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+import re
+
+model_name_or_path = os.environ['MODEL_PATH']
+# model_name_or_path = "tencent/Hunyuan-A13B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto",trust_remote_code=True)  # You may want to use bfloat16 and/or move to GPU here
+messages = [
+    {"role": "user", "content": "Write a short summary of the benefits of regular exercise"},
+]
+
+text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            enable_thinking=True
+            )
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+model_inputs.pop("token_type_ids", None)
+outputs = model.generate(**model_inputs, max_new_tokens=4096)
+
+
+output_text = tokenizer.decode(outputs[0])
+
+think_pattern = r'<think>(.*?)</think>'
+think_matches = re.findall(think_pattern, output_text, re.DOTALL)
+
+answer_pattern = r'<answer>(.*?)</answer>'
+answer_matches = re.findall(answer_pattern, output_text, re.DOTALL)
+
+think_content = [match.strip() for match in think_matches][0]
+answer_content = [match.strip() for match in answer_matches][0]
+print(f"thinking_content:{think_content}\n\n")
+print(f"answer_content:{answer_content}\n\n")

model.properties

+# 模型唯一标识
+modelCode=1748
+# 模型名称
+modelName=Hunyuan-A13B-vllm
+# 模型描述
+modelDescription=腾讯发布的Hunyuan-A13B模型，不仅在效果上达到了高标准，而且在尺寸上也做到了极致的优化，成功平衡了模型性能与资源占用。
+# 应用场景
+appScenario=推理,制造,广媒,家居,教育
+# 框架类型
+frameType=vllm