# Best Practices for ORPO Algorithm
The ORPO algorithm requires the same data format as DPO. Beyond SFT data [query, response], it additionally requires `rejected_response` to denote answers that the model should not generate.

The ORPO algorithm incorporates an odds ratio (OR) negative log-likelihood loss term into the loss function used during SFT training, to reduce the probability of generating rejected responses.

Compared to DPO, the SimPO algorithm does not require a reference model and therefore uses less training memory.

Here, the hyperparameter beta represents the coefficient for the OR loss term. The larger the beta, the greater the penalty on rejected_response, with a default value of 0.1.

This best practice session will train the [llama3-8b-instruct](https://modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct/summary) model using the ORPO algorithm, enabling it to respond in Chinese.

Table of Contents
- [Environment Preparation](#environment-preparation)
- [Dataset](#dataset)
- [Training](#training)
- [Inference](#inference)

## Environment Preparation

```shell
# Install ms-swift
git clone https://github.com/modelscope/swift.git
cd swift
pip install -e '.[llm]'
# Environment alignment (usually no need to run. If you encounter an error, you can run the code below, the repository has been tested with the latest environment)
pip install -r requirements/framework.txt  -U
pip install -r requirements/llm.txt  -U
```
## Dataset
We use the [Llama3-DPO-zh-en-emoji](https://modelscope.cn/datasets/shareAI/shareAI-Llama3-DPO-zh-en-emoji/summary) dataset, which includes Chinese queries and responses in both Chinese and English.
Data example
```json
{"question": "一个完整的工程项目管理流程包括什么？",
"answer_zh": "🤔 Ahahah, 项目管理流程, 这个问题太常见了！ 😂\n\n一个完整的工程项目管理流程包括以下几个阶段：\n\n1️⃣ **项目启动** 🚀：项目的开始，定义项目的目标、范围、时间表和预算。\n\n2️⃣ **项目计划** 📅：制定项目计划，包括任务分解、资源分配、进度安排和风险管理。\n\n3️⃣ **项目执行** 💪：按照计划执行项目，包括任务执行、进度控制和质量管理。\n\n4️⃣ **项目监控** 👀：对项目的进度、成本和质量进行监控和控制，确保项目不偏离轨道。\n\n5️⃣ **项目收尾** 🎉：项目完成后，进行项目总结、经验分享和文档编制。\n\n6️⃣ **项目评价** 🤔：对项目的成果和过程进行评价，总结经验和教训。\n\n这些阶段是一个完整的工程项目管理流程，但是，具体的项目可能会根据需要添加或删除某些阶段。 🤝\n\n还有一点，项目管理流程中需要注意的几个关键点是：\n\n* **沟通** 💬：项目团队之间、项目团队和客户之间的沟通是非常重要的。\n* **风险管理** ⚠️：项目中总会出现一些风险和问题，需要提前预测和准备。\n* **变更管理** 🔄：项目中可能会出现一些变更，需要及时处理和更新。\n\n总之，项目管理流程是一个复杂的过程，需要项目经理和团队成员的共同努力和合作。 💪",
"answer_en": "My fellow wizarding friend! 🧙‍♂️\n\nA complete project management process, you ask? Well, I shall conjure up the essential steps for thee! 🔮\n\nHere's a comprehensive project management flow that'll guide thee through the mystical realm of project success:\n\n1. **Initiation**: Define project scope, goals, and deliverables. Identify stakeholders, and determine the project's feasibility. ⚡️\n2. **Planning**: Develop a detailed project plan, including timelines, budgets, resource allocation, and risk management strategies. 📅\n3. **Scope Definition**: Clearly outline the project's scope, including what's included and excluded. This will help prevent scope creep, my friend! 🔒\n4. **Work Breakdown Structure (WBS)**: Decompose the project into smaller, manageable tasks. This will aid in task assignment, scheduling, and tracking progress. 🗂️\n5. **Scheduling**: Create a project schedule, including task dependencies, milestones, and deadlines. This will help thee stay on track and avoid procrastination! ⏰\n6. **Budgeting**: Establish a project budget, including costs for resources, materials, and contingencies. This will ensure thou dost not overspend, my wise friend! 💸\n7. **Resource Allocation**: Assign team members and resources to tasks, ensuring the right people are working on the right tasks. This will optimize productivity and efficiency! 👥\n8. **Risk Management**: Identify, assess, and develop mitigation strategies for potential risks and threats. This will safeguard thy project from unforeseen dangers! ⚠️\n9. **Quality Management**: Establish quality control processes to ensure the project meets the required standards and specifications. This will guarantee a top-notch outcome, my friend! ✨\n10. **Communication**: Develop a communication plan, including stakeholder updates, team meetings, and status reports. This will keep everyone informed and aligned! 📢\n11. **Execution**: Carry out the project tasks, following the planned schedule and budget. This is where the magic happens, my friend! ✨\n12. **Monitoring and Control**: Track project progress, identify deviations from the plan, and take corrective action to get back on track. This will ensure thy project stays on course! 📊\n13. **Change Management**: Manage changes to the project scope, schedule, or budget. This will minimize disruptions and ensure the project remains viable! 🔀\n14. **Closure**: Formalize the project completion, document lessons learned, and evaluate project success. This will bring a sense of accomplishment and closure, my friend! 🎉\n\nThere thou hast it, my fellow wizard! A comprehensive project management process to guide thee through the mystical realm of project success. May thy projects be prosperous and thy stakeholders be delighted! 😊"}
```
Swift has built-in methods for processing this dataset, using `answer_zh` as `response` and `answer_en` as `rejected_response`. Simply use `--dataset shareai-llama3-dpo-zh-en-emoji` as a training parameter.

## Training

```shell
# Experimental environment: A100
# DDP + MP
# Memory usage: 4*24G
nproc_per_node=2

CUDA_VISIBLE_DEVICES=0,1,2,3 \
NPROC_PER_NODE=$nproc_per_node \
MASTER_PORT=29500 \
swift rlhf \
    --rlhf_type orpo \
    --model_type  llama3-8b-instruct \
    --beta 0.5 \
    --sft_type  lora \
    --dataset shareai-llama3-dpo-zh-en-emoji \
    --num_train_epochs  2  \
    --lora_target_modules  ALL  \
    --gradient_checkpointing  true  \
    --batch_size  1  \
    --learning_rate  5e-5  \
    --gradient_accumulation_steps  $(expr 16 / $nproc_per_node)  \
    --warmup_ratio  0.03  \
    --save_total_limit  2

# MP(device map)
# Memory usage: 2*24G
CUDA_VISIBLE_DEVICES=0,1 \
swift rlhf \
    --rlhf_type orpo \
    --model_type  llama3-8b-instruct \
    --beta 0.5 \
    --sft_type  lora \
    --dataset shareai-llama3-dpo-zh-en-emoji \
    --num_train_epochs  2  \
    --lora_target_modules  ALL  \
    --gradient_checkpointing  true  \
    --batch_size  1  \
    --learning_rate  5e-5  \
    --gradient_accumulation_steps  16  \
    --warmup_ratio  0.03  \
    --save_total_limit  2

# Memory usage: 40G
CUDA_VISIBLE_DEVICES=0 \
swift rlhf \
    --rlhf_type orpo \
    --model_type  llama3-8b-instruct \
    --beta 0.5 \
    --sft_type  lora \
    --dataset shareai-llama3-dpo-zh-en-emoji \
    --num_train_epochs  2  \
    --lora_target_modules  ALL  \
    --gradient_checkpointing  true  \
    --batch_size  1  \
    --learning_rate  5e-5  \
    --gradient_accumulation_steps  16  \
    --warmup_ratio  0.03  \
    --save_total_limit  2
```

**Notes**:

- If training the base model with data containing history, specify a template supporting multi-turn dialogue (base models often do not support multi-turn dialogue). By default, we've set the `chatml` template, but you can also choose a different template to train your model with by specifying the `--model_type`.
- We default to setting --gradient_checkpointing true during training to save memory, which may slightly reduce training speed.
- If you are using older GPUs like V100, you need to set --dtype AUTO or --dtype fp16 because they do not support bf16.
- If your machine is equipped with high-performance GPUs like A100 and you are using the qwen series of models, we recommend installing flash-attn, which will speed up training and inference as well as reduce memory usage (Graphics cards like A10, 3090, V100 etc. do not support training with flash-attn). Models that - support flash-attn can be viewed in LLM Supported Models.
- If you need to train offline, please use --model_id_or_path <model_dir> and set --check_model_is_latest false. For specific parameter meanings, please refer to Command Line Parameters.
- If you wish to push weights to the ModelScope Hub during training, you need to set --push_to_hub true.
## Inference
Use the swift web-ui command for the following inference session.

### Pre-Training Inference
> 你是谁(Who are you)

![orpo1](../../resources/orpo1.png)

> 西湖醋鱼怎么做(How do you make West Lake Vinegar Fish?)

![orpo2](../../resources/orpo2.png)
![orpo3](../../resources/orpo3.png)
![orpo4](../../resources/orpo4.png)
![orpo5](../../resources/orpo5.png)


### Post-Training Inference
> 你是谁(Who are you)

![orpo6](../../resources/orpo6.png)

> 西湖醋鱼怎么做(How do you make West Lake Vinegar Fish?)

![orpo7](../../resources/orpo7.png)
![orpo8](../../resources/orpo8.png)
