> ## Documentation Index > Fetch the complete documentation index at: https://docs.monostate.ai/llms.txt > Use this file to discover all available pages before exploring further. # 配置模板 > 即用配置模板 # 配置模板 复制这些模板并根据您的用例进行自定义。 ## LLM 训练模板 ### 快速 SFT（小模型） 最适合测试和小数据集： ```yaml theme={null} task: llm-sft backend: local base_model: google/gemma-3-270m project_name: quick-sft data: path: ./data.jsonl train_split: train valid_split: null chat_template: tokenizer column_mapping: text_column: text log: wandb params: epochs: 3 batch_size: 4 lr: 3e-5 ``` ### 生产 SFT（7B 模型） 用于使用 LoRA 进行全量微调： ```yaml theme={null} task: llm-sft backend: local base_model: meta-llama/Llama-3.2-8B project_name: production-sft data: path: ./conversations.jsonl train_split: train valid_split: validation chat_template: tokenizer column_mapping: text_column: text log: wandb hub: push_to_hub: false params: epochs: 3 batch_size: 2 gradient_accumulation: 8 lr: 3e-5 warmup_ratio: 0.1 mixed_precision: bf16 peft: true lora_r: 32 lora_alpha: 64 lora_dropout: 0.05 use_flash_attention_2: true packing: true save_strategy: steps save_steps: 500 save_total_limit: 1 ``` ### DPO 偏好训练 用于偏好对齐： ```yaml theme={null} task: llm-dpo backend: local base_model: meta-llama/Llama-3.2-1B project_name: dpo-aligned data: path: ./preferences.jsonl train_split: train valid_split: null chat_template: tokenizer column_mapping: prompt_text_column: prompt text_column: chosen rejected_text_column: rejected log: wandb params: dpo_beta: 0.1 max_prompt_length: 128 max_completion_length: null epochs: 1 batch_size: 2 gradient_accumulation: 4 lr: 5e-6 peft: true lora_r: 16 lora_alpha: 32 ``` ### ORPO 训练 结合 SFT + 偏好优化： ```yaml theme={null} task: llm-orpo backend: local base_model: google/gemma-2-2b project_name: gemma-orpo data: path: ./preferences.jsonl train_split: train valid_split: null chat_template: tokenizer column_mapping: prompt_text_column: prompt text_column: chosen rejected_text_column: rejected log: wandb params: dpo_beta: 0.1 max_prompt_length: 128 max_completion_length: null epochs: 3 batch_size: 2 lr: 5e-5 peft: true lora_r: 16 ``` ### 知识蒸馏 用于压缩更大的模型： ```yaml theme={null} task: llm-sft backend: local base_model: google/gemma-3-270m project_name: distilled-gemma data: path: ./prompts.jsonl train_split: train valid_split: null chat_template: tokenizer column_mapping: text_column: text log: wandb params: use_distillation: true teacher_model: google/gemma-2-2b distill_temperature: 3.0 distill_alpha: 0.7 epochs: 5 batch_size: 8 lr: 1e-4 ``` ## 分类模板 ### 文本分类 ```yaml theme={null} task: text-classification backend: local base_model: bert-base-uncased project_name: sentiment data: path: ./reviews.csv train_split: train valid_split: null column_mapping: text_column: text target_column: target log: wandb params: epochs: 5 batch_size: 16 lr: 5e-5 ``` ### 多类分类 ```yaml theme={null} task: text-classification backend: local base_model: microsoft/deberta-v3-base project_name: categorizer data: path: ./categories.csv train_split: train valid_split: validation column_mapping: text_column: content target_column: target log: wandb params: epochs: 10 batch_size: 8 lr: 1e-5 warmup_ratio: 0.1 ``` ### 标记分类（NER） ```yaml theme={null} task: token-classification backend: local base_model: bert-base-cased project_name: entity-extractor data: path: ./ner_data.json train_split: train valid_split: null column_mapping: tokens_column: tokens tags_column: tags log: wandb params: epochs: 5 batch_size: 16 lr: 5e-5 ``` ## 视觉模板 ### 图像分类 ```yaml theme={null} task: image-classification backend: local base_model: google/vit-base-patch16-224 project_name: image-classifier data: path: ./images/ train_split: train valid_split: null column_mapping: image_column: image target_column: target log: wandb params: epochs: 10 batch_size: 32 lr: 5e-5 ``` ### 目标检测 ```yaml theme={null} task: object-detection backend: local base_model: facebook/detr-resnet-50 project_name: detector data: path: ./coco_format/ train_split: train valid_split: null column_mapping: image_column: image objects_column: objects log: wandb params: epochs: 20 batch_size: 8 lr: 1e-4 ``` ### 视觉-语言模型（VQA） ```yaml theme={null} task: vlm:vqa backend: local base_model: google/paligemma-3b-pt-224 project_name: vlm-vqa data: path: ./vqa_data.jsonl train_split: train valid_split: null column_mapping: image_column: image text_column: text prompt_text_column: prompt log: wandb params: epochs: 3 batch_size: 2 lr: 5e-5 gradient_accumulation: 4 peft: true lora_r: 16 lora_alpha: 32 ``` ## 高级模板 ### 超参数扫描 ```yaml theme={null} task: llm-sft backend: local base_model: google/gemma-3-270m project_name: sweep-experiment data: path: ./data.jsonl train_split: train valid_split: validation chat_template: tokenizer column_mapping: text_column: text log: wandb params: use_sweep: true sweep_backend: optuna sweep_n_trials: 20 sweep_metric: eval_loss sweep_direction: minimize # Base parameters (sweep will vary these) epochs: 3 batch_size: 4 lr: 3e-5 peft: true lora_r: 16 ``` ### 句子转换器 ```yaml theme={null} task: sentence-transformers:pair_score backend: local base_model: sentence-transformers/all-MiniLM-L6-v2 project_name: embeddings data: path: ./pairs.csv train_split: train valid_split: null column_mapping: sentence1_column: sentence1 sentence2_column: sentence2 target_column: score log: wandb params: epochs: 3 batch_size: 8 lr: 3e-5 ``` ## 用法 将任何模板保存为 `config.yaml` 并运行： ```bash theme={null} aitraining --config config.yaml ``` ## 下一步 配置文件结构详情 参数参考