reward model训练的重载以及评测

[yata0]

Reminder

• I have read the README and searched the existing issues.

System Info

• Platform: Linux-5.4.143.bsk.8-amd64-x86_64-with-glibc2.31
• Python version: 3.10.13
• PyTorch version: 2.2.2+cu121 (GPU)
• Transformers version: 4.42.3
• Datasets version: 2.18.0
• Accelerate version: 0.32.1
• PEFT version: 0.11.1
• TRL version: 0.9.6
• GPU type: Tesla V100-SXM2-32GB

Reproduction

1. 导出：lamafactory-cli export --model_name_or_path=“./save” --stage=rm --export_dir="./see12" --template=default
   

2. 测试：

from trl import AutoModelForCausalLMWithValueHead
model_path = "./see12"
model = AutoModelForCausalLMWithValueHead.from_pretrained(model_path, trust_remote_code=True)

报错：
v_head weight is found. This IS expected if you are not resuming PPO training

#4379 (comment)

Expected behavior

No response

Others

No response

[hiyouga]

仅支持用 llamafactory 加载 RM
llamafactory-cli api --model_name_or_path xx --template xx --stage rm

[yata0]

llamafactory-cli api --model_name_or_path xx --template xx --stage rm

怎么对rm做评测呢？

[yata0]

llamafactory-cli api --model_name_or_path xx --template xx --stage rm

怎么对rm做评测呢？

@hiyouga

[hiyouga]

把训练脚本里的 do_train 改成 do_eval

[xd2333]

我知道了，修改yaml
do_train: false
do_eval: false
do_predict: true
adapter_name_or_path: 训练后的lora

奖励预测结果会在output_dir里

[bruceguo123]

@hiyouga @xd2333
只输出了100个结果，为什么呢？数据中有1000条数据的。

命令： llamafactory-cli train /root/autodl-tmp/llm_prj/AdGen/config/reward_infer_model.yaml
reward_infer_model.yaml文件内容：

model

model_name_or_path: /root/autodl-tmp/llm_prj/AdGen/reward_model/merge

method

stage: rm
do_train: false
do_eval: false
do_predict: true

do_train: false
do_eval: false
do_predict: true

dataset

dataset: ad_dpo
template: qwen
cutoff_len: 1024
max_samples: 10000
overwrite_cache: true
preprocessing_num_workers: 16

output

output_dir: /root/autodl-tmp/llm_prj/AdGen/reward_model/infer
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true

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

eval

val_size: 0.1
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 500

dataset: ad_dpo配置如下：
"ad_dpo": {
"file_name": "/root/autodl-tmp/llm_prj/AdGen/data/dpo/ad_dpo.jsonl",
"ranking": true,
"columns": {
"prompt": "instruction",
"chosen": "chosen",
"rejected": "rejected"
}
}

输出：

[xd2333]

@hiyouga @xd2333 只输出了100个结果，为什么呢？数据中有1000条数据的。

命令： llamafactory-cli train /root/autodl-tmp/llm_prj/AdGen/config/reward_infer_model.yaml reward_infer_model.yaml文件内容：

model

model_name_or_path: /root/autodl-tmp/llm_prj/AdGen/reward_model/merge

method

stage: rm do_train: false do_eval: false do_predict: true

do_train: false do_eval: false do_predict: true

dataset

dataset: ad_dpo template: qwen cutoff_len: 1024 max_samples: 10000 overwrite_cache: true preprocessing_num_workers: 16

output

output_dir: /root/autodl-tmp/llm_prj/AdGen/reward_model/infer logging_steps: 10 save_steps: 500 plot_loss: true overwrite_output_dir: true

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

eval

val_size: 0.1 per_device_eval_batch_size: 1 eval_strategy: steps eval_steps: 500

dataset: ad_dpo配置如下： "ad_dpo": { "file_name": "/root/autodl-tmp/llm_prj/AdGen/data/dpo/ad_dpo.jsonl", "ranking": true, "columns": { "prompt": "instruction", "chosen": "chosen", "rejected": "rejected" } }

输出：

设置eval_dataset: ad_dpo，删除val_size: 0.1、eval_strategy: steps、eval_steps: 500

[rover5056]

仅支持用 llamafactory 加载 RM llamafactory-cli api --model_name_or_path xx --template xx --stage rm

请问请求多模态的标准的请求脚本可以提供一个 demo case么～ 试了半天不知道怎么拼接 message。。。
使用这个启动的：
llamafactory-cli api --stage rm --template qwen2_vl --model_name_or_path models/qwen2_vl_rm_lora_1027_3sets

或者使用 trl 库的话该怎么加载模型推理得到分数～

感谢感谢～

@hiyouga @xd2333

[world2025]

请问一下，训练reward model支持这样的数据格式吗 openbookqa，一个prompt+多个response，跟Instructgpt一样