First commit

Contributors.md

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

LICENSE

+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright 2024 GLM-4-9B Model Team @ Zhipu AI
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

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。
+## 模型结构
\ No newline at end of file
+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