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Contributors.md

+# Contributors
+None
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README.md

 # Ministral_pytorch
+## 论文
+暂无
 
-专为边缘设备和边缘计算场景而设计，支持高达128，000个 token 的上下文长度。
\ No newline at end of file
+## 模型结构
+Ministral 8B 架构的详细信息如下：
+<div align=center>
+    <img src="./doc/model.png"/>
+</div>
+
+## 算法原理
+Mistral AI为本地智能、设备计算和边缘用例引入了两种新的先进模型Ministral 3B 和 Ministral 8B。
+主要特点有：
+- 使用 128k 上下文窗口和交错滑动窗口注意力进行训练
+- 在大量多语言和代码数据上进行训练
+- 支持函数调用，词汇量为 131k，使用 V3-Tekken 标记化器
+
+
+## 环境配置
+`-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/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250521-fixpy-rocblas0521-beta2
+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/ministral_pytorch
+
+```
+
+### Dockerfile（方法二）
+```bash
+cd docker
+docker build --no-cache -t ministral: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/ministral_pytorch
+
+```
+
+### 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.hpccube.com/tool/)开发者社区下载安装。
+```bash
+DTK: 25.04
+python: 3.10
+torch: 2.4.1+das.opt2.dtk2504
+vllm: 0.8.5
+deepspeed: 0.14.2+das.opt2.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
+### Llama Factory 微调方法(推荐)
+
+1. 训练库安装（**非ministral_pytorch目录下**），`Llama-Factory`具体安装方法请参考仓库的README。
+```
+git clone https://developer.sourcefind.cn/codes/OpenDAS/llama-factory
+```
+
+2. 通过[预训练权重](#预训练权重)下载预训练模型，当前用例使用[Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410)模型。
+
+#### 全参微调
+
+SFT训练脚本示例，参考`llama-factory/train_full`下对应yaml文件。
+
+**参数修改**：
+- **--model_name_or_path**: 修改为待训练模型地址，如 `/data/Ministral-8B-Instruct-2410`
+- **--dataset**: 微调训练集名称，可选数据集请参考 `llama-factory/data/dataset_info.json`
+- **--template**: 将 default 修改为 `ministral`
+- **--output_dir**: 模型保存地址
+
+其他参数如：`--learning_rate`、`--save_steps`可根据自身硬件及需求进行修改。
+
+#### lora微调
+
+SFT训练脚本示例，参考`llama-factory/train_lora`下对应yaml文件。
+参数解释同[#全参微调](#全参微调)
+
+## 推理
+### vllm推理方法
+#### offline
+```bash
+## 必须添加HF_ENDPOINT环境变量
+export HF_ENDPOINT=https://hf-mirror.com
+
+python infer_vllm.py --model_name mistralai/Ministral-8B-Instruct-2410
+```
+
+#### server
+
+1. 启动server
+```bash
+vllm serve mistralai/Ministral-8B-Instruct-2410 --tokenizer_mode mistral --config_format mistral --load_format mistral
+```
+
+2. client测试:
+```bash
+curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer token' \
+--data '{
+    "model": "mistralai/Ministral-8B-Instruct-2410",
+    "messages": [
+      {
+        "role": "user",
+        "content": "Do we need to think for 10 seconds to find the answer of 1 + 1?"
+      }
+    ]
+}'
+
+```
+
+## result
+<div align=center>
+    <img src="./doc/results.png"/>
+</div>
+
+### 精度
+暂无
+
+## 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
+- [Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410)
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/ministral_pytorch
+
+## 参考资料
+- https://mistral.ai/news/ministraux
\ No newline at end of file

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250521-fixpy-rocblas0521-beta2
\ No newline at end of file

infer_vllm.py

+# This example shows how to use vLLM to run the Ministral 8B model.
+import argparse
+
+from vllm import LLM
+from vllm.sampling_params import SamplingParams
+
+
+parse = argparse.ArgumentParser()
+parse.add_argument("--model_name", type=str, default="mistralai/Ministral-8B-Instruct-2410")
+parse.add_argument("--prompt", type=str, default="Do we need to think for 10 seconds to find the answer of 1 + 1?")
+args = parse.parse_args()
+
+sampling_params = SamplingParams(max_tokens=8192)
+
+# note that running Ministral 8B on a single GPU requires 24 GB of GPU RAM
+# If you want to divide the GPU requirement over multiple devices, please add *e.g.* `tensor_parallel=2`
+llm = LLM(model=args.model_name, tokenizer_mode="mistral", config_format="mistral", load_format="mistral")
+
+messages = [
+    {
+        "role": "user",
+        "content": args.prompt
+    },
+]
+
+outputs = llm.chat(messages, sampling_params=sampling_params)
+
+print("output:", outputs[0].outputs[0].text)

llama-factory/train_full/ministral_full_sft.yaml

+### model
+model_name_or_path: mistralai/Ministral-8B-Instruct-2410
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+deepspeed: examples/deepspeed/ds_z3_config.json  # choices: [ds_z0_config.json, ds_z2_config.json, ds_z3_config.json]
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: ministral
+cutoff_len: 2048
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/ministral/full/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+save_only_model: false
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 2
+learning_rate: 1.0e-5
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+
+### eval
+# eval_dataset: alpaca_en_demo
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500

llama-factory/train_lora/ministral_lora_sft.yaml

+### model
+model_name_or_path: mistralai/Ministral-8B-Instruct-2410
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_rank: 8
+lora_target: all
+deepspeed: examples/deepspeed/ds_z3_config.json  # choices: [ds_z0_config.json, ds_z2_config.json, ds_z3_config.json]
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: ministral
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/ministral/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+save_only_model: false
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+
+### eval
+# eval_dataset: alpaca_en_demo
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500

model.properties

+# 模型唯一标识
+modelCode=1595
+# 模型名称
+modelName=ministral_pytorch
+# 模型描述
+modelDescription=Mistral AI专为边缘设备和边缘计算场景而设计的模型，支持高达128，000个 token 的上下文长度。
+# 应用场景
+appScenario=推理,训练,对话问答,制造,广媒,家居,教育
+# 框架类型
+frameType=pytorch