First commit

Contributors.md

+# Contributors
+None
\ No newline at end of file

LICENSE

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README.md

-# Mistral-Small_pytorch
+# Mistral-Small
+## 论文
+暂无
 
+## 模型结构
+基于transformer结构
+<div align=center>
+    <img src="./doc/transformers.jpg" witdh=300 height=400/>
+</div>
+
+## 算法原理
+Mistral Small 3.1 (25.03) 是一款多用途模型，专为编程、数学推理、文档理解和对话等各种任务而设计。2503 增加了最先进的视觉理解，在不影响文本性能的情况下增强了长达128k个tokens的长上下文功能。该模型有240亿个参数，在文本和视觉任务中都达到了顶级的能力。
+
+- 视觉：视觉功能使模型能够分析图像，并提供基于文本之外的视觉内容的见解。
+- 多语言：支持数十种语言，包括英语，法语，德语，希腊语，印地语，印度尼西亚语，意大利语，日语，韩语，马来语，尼泊尔语，波兰语，葡萄牙语，罗马尼亚语，俄语，塞尔维亚语，西班牙语，瑞典语，土耳其语，乌克兰语，越南语，阿拉伯语，孟加拉语，中文，波斯语。
+- 以代理为中心：提供一流的代理功能，具备原生函数调用和 JSON 输出能力。
+- 高级推理：最先进的对话和推理能力。
+- 分词器：使用了一个词汇量为 131k 的 Tekken tokenizer
+
+
+## 环境配置
+`-v 路径`、`docker_nam`e和`imageID`根据实际情况修改
+
+### Docker（方法一）
+```bash
+dcoker 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/mistral-small_pytorch
+pip install mistral_common>=1.5.4
+```
+
+### Dockerfile（方法二）
+```bash
+cd docker
+docker build --no-cache -t mistral-small: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/mistral-small_pytorch
+pip install mistral_common>=1.5.4
+```
+
+### 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
+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`
+
+其它非深度学习库安装方式如下：
+```bash
+pip install mistral_common>=1.5.4
+```
+
+## 数据集
+无
+
+## 训练
+### Llama Factory 微调方法(推荐)
+1. 训练库安装（**非mistral-small_pytorch目录下**），安装**主线版本**，`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[Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503)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/mistralai/Mistral-Small-3.1-24B-Instruct-2503`
+- **--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 `mistral_small`
+- **--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
+python infer_vllm.py --model_name_or_path /path_of/model
+```
+
+#### server
+```bash
+vllm serve mistralai/Mistral-Small-3.1-24B-Instruct-2503 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' -tp 2 --port 8001 --trust-remote-code --enforce-eager --served-model-name Mistral-Small
+```
+
+```bash
+## 必须添加HF_ENDPOINT环境变量
+export HF_ENDPOINT=https://hf-mirror.com
+python infer_client.py
+```
+
+## 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
+- [Mistral-Small-3.1-24B-Base-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503)
+- [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503)
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/mistral-small_pytorch
+
+## 参考资料
+- https://huggingface.co/mistralai
+- https://github.com/hiyouga/LLaMA-Factory/

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_client.py

+import requests
+import json
+from huggingface_hub import hf_hub_download
+from datetime import datetime, timedelta
+
+url = "http://<your-server-url>:8000/v1/chat/completions"
+headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
+
+model = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
+
+
+def load_system_prompt(repo_id: str, filename: str) -> str:
+    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
+    with open(file_path, "r") as file:
+        system_prompt = file.read()
+    today = datetime.today().strftime("%Y-%m-%d")
+    yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
+    model_name = repo_id.split("/")[-1]
+    return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
+
+
+SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
+
+image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"
+
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "Which of the depicted countries has the best food? Which the second and third and fourth? Name the country, its color on the map and one its city that is visible on the map, but is not the capital. Make absolutely sure to only name a city that can be seen on the map.",
+            },
+            {"type": "image_url", "image_url": {"url": image_url}},
+        ],
+    },
+]
+
+data = {"model": model, "messages": messages, "temperature": 0.15}
+
+response = requests.post(url, headers=headers, data=json.dumps(data))
+print(response.json()["choices"][0]["message"]["content"])

infer_vllm.py

+import argparse
+
+from vllm import LLM
+from vllm.sampling_params import SamplingParams
+
+# This script demonstrates how to use vLLM to run inference with a Mistral model.
+parse = argparse.ArgumentParser()
+parse.add_argument("--user_prompt", type=str, default="Give me 5 non-formal ways to say 'See you later' in French.")
+parse.add_argument("--model_name_or_path", type=str, default="mistralai/Mistral-Small-3.1-24B-Instruct-2503")
+
+args = parse.parse_args()
+
+# Define the system prompt for the conversational agent
+SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
+
+messages = [
+    {
+        "role": "system",
+        "content": SYSTEM_PROMPT
+    },
+    {
+        "role": "user",
+        "content": args.user_prompt
+    },
+]
+
+# note that running this model on GPU requires over 60 GB of GPU RAM
+llm = LLM(model=args.model_name_or_path, tokenizer_mode="mistral")
+
+sampling_params = SamplingParams(max_tokens=512, temperature=0.15)
+outputs = llm.chat(messages, sampling_params=sampling_params)
+
+print(outputs[0].outputs[0].text)
\ No newline at end of file

llama-factory/train_full/mistral_full_sft.yaml

+### model
+model_name_or_path: mistralai/Mistral-Small-3.1-24B-Instruct-2503
+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: mistral_small
+cutoff_len: 2048
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/mistral_small/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/mistral_lora_sft.yaml

+### model
+model_name_or_path: mistralai/Mistral-Small-3.1-24B-Instruct-2503
+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: mistral_small
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/mistral_small/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=1591
+# 模型名称
+modelName=mistral-small_pytorch
+# 模型描述
+modelDescription=Mistral Small 3.1 (25.03) 是一款多用途模型，专为编程、数学推理、文档理解和对话等各种任务而设计。
+# 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