多模态大模型微调框架之Llama-factory

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多模态大模型微调框架之Llama-factory

LlamaFactory Online 是一个面向科研机构、企业研发团队或个人开发者快速构建和部署AI应用的一站式大模型训练与微调平台，致力于提供简单易用、高效灵活的全流程解决方案。平台以“低门槛、高效率、强扩展”为核心，通过集成化工具链、可视化操作界面与自动化工作流，显著降低大模型定制与优化的技术成本，助力用户快速实现模型从开发调试到生产部署的全周期闭环，功能示意如下所示。

官方文档：

https://llamafactory.readthedocs.io/zh-cn/latest/

安装

使用 uv 工具来安装 Llama-factory

下载工程

git clone --depth 1 https://github.com/hiyouga/LlamaFactory.git

uv 安装

cd LlamaFactory uv sync

使用一条命令uv sync就完成 LlamaFactory 的安装，版本以及依赖版本等不会从错误

验证

打开 llamafactory 自带的web页面

uv run llamafactory-cli webui

能正常打开这个页面就说明安装没有问题了

简单使用

llamafactory 的使用有两种模型，分别是web页面和命令行。这里就简单介绍一下命令行的使用。

基本功能的命令行使用包括：

  1. 训练
  2. 导出
  3. 推理
  4. 评估

命令行的通用使用方式是 llamafactory-cli + 任务 + 配置文件

任务类型主要通过任务来指定，如：

  • train：训练
  • export：导出
  • chat：推理
  • eval：评估

配置文件是yaml格式的文件，命名也很清晰，包括训练参数，任务配置参数。

在训练上，官方给了很多示例文件，比如 全量训练、lora微调、qlora微调等方法。

训练

uv run llamafactory-cli train examples/train_lora/qwen3_lora_sft.yaml 
### examples/train_lora/llama3_lora_sft.yaml model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct stage: sft do_train: true finetuning_type: lora lora_target:all dataset: identity,alpaca_en_demo template: llama3 cutoff_len:1024 max_samples:1000 overwrite_cache: true preprocessing_num_workers:16 output_dir: saves/llama3-8b/lora/sft logging_steps:10 save_steps:500 plot_loss: true overwrite_output_dir: true 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 val_size:0.1 per_device_eval_batch_size:1 eval_strategy: steps eval_steps:500

导出：

llamafactory-cli export merge_config.yaml 
### examples/merge_lora/llama3_lora_sft.yaml### model model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct adapter_name_or_path: saves/llama3-8b/lora/sft template: llama3 finetuning_type: lora ### export export_dir: models/llama3_lora_sft export_size:2 export_device: cpu export_legacy_format: false

推理：

llamafactory-cli chat inference_config.yaml 
### examples/inference/llama3.yaml model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct template: llama3 infer_backend: huggingface #choices： [huggingface, vllm]

评估

llamafactory-cli eval examples/train_lora/llama3_lora_eval.yaml 
### examples/train_lora/llama3_lora_eval.yaml### model model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct adapter_name_or_path: saves/llama3-8b/lora/sft # 可选项### method finetuning_type: lora ### dataset task: mmlu_test # mmlu_test, ceval_validation, cmmlu_test template: fewshot lang: en n_shot:5### output save_dir: saves/llama3-8b/lora/eval### eval batch_size:4

微调 Qwen3 VL

模型准备

llamafactory-cli 可以自动下载模型，但是国内有时会超时，建议使用国内镜像网站。在命令行中执行如下：

export HF_ENDPOINT="https://hf-mirror.com"

选择一个指定的模型 Qwen/Qwen3-VL-2B-Instruct

数据准备

llamafactory 中数据集的配置集中在 data 下面的 dataset_info.json

"identity":{"file_name":"identity.json"},"alpaca_en_demo":{"file_name":"alpaca_en_demo.json"},"alpaca_zh_demo":{"file_name":"alpaca_zh_demo.json"},"glaive_toolcall_en_demo":{"file_name":"glaive_toolcall_en_demo.json","formatting":"sharegpt","columns":{"messages":"conversations","tools":"tools"}},

dataset_info.json 中json格式的文件，配置了需要使用的数据集。key 是数据集的名字，value 是具体参数。

例如：

数据集名称：alpaca_en_demo

数据集路径：alpaca_en_demo.json

具体数据集的格式，llamafactory 目前支持 alpaca 和sharegpt两种数据格式。

alpaca：

{{"instruction":"Describe a process of making crepes.","input":"","output":"Making crepes is an easy and delicious process! Enjoy!"},{"instruction":"Transform the following sentence using a synonym: The car sped quickly.","input":"","output":"The car accelerated rapidly."},....}

sharegpt：

{{"messages":[{"content":"<audio>What's that sound?","role":"user"},{"content":"It is the sound of glass shattering.","role":"assistant"}],"audios":["mllm_demo_data/1.mp3"]}...}

本次使用 coco-2014-caption，属于sharegpt格式，所以使用sharegpt格式来准备。

dataset_info.json 注册 coco数据集的配置项

"coco-400":{"file_name":"coco-400.json","formatting":"sharegpt","columns":{"messages":"conversations","id":"id"},"tags":{"role_tag":"from","content_tag":"value","user_tag":"user","assistant_tag":"assistant"}}

coco 数据集的格式如下：

配置参数

微调就选择qlora的方式，根据工程给的示例文件去修改，选择的示例文件是：

### model model_name_or_path: Qwen/Qwen3-4B-Instruct-2507 quantization_bit:4# choices: [8 (bnb/hqq/eetq), 4 (bnb/hqq), 3 (hqq), 2 (hqq)] quantization_method: bnb # choices: [bnb, hqq, eetq] trust_remote_code: true ### method stage: sft do_train: true finetuning_type: lora lora_rank:8 lora_target:all### dataset dataset: identity,alpaca_en_demo template: qwen3_nothink cutoff_len:2048 max_samples:1000 preprocessing_num_workers:16 dataloader_num_workers:4### output output_dir: saves/qwen3-4b/lora/sft logging_steps:10 save_steps:500 plot_loss: true overwrite_output_dir: true save_only_model: false report_to: none # choices: [none, wandb, tensorboard, swanlab, mlflow]### 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

根据以上模板，修改成我们自身的参数，关键修改在于：

  1. 模型名称： model_name_or_path
  2. 数据集：dataset
  3. 模板：template 视觉大模型是qwen3_vl_nothink 语言大模型是qwen3_nothink，用错模板会报错

剩下的如训练批次、batch_size、梯度累计、学习率、保存路径、训练记录等都有设置，不再详说

### model model_name_or_path: Qwen/Qwen3-VL-2B-Instruct quantization_bit:4# choices: [8 (bnb/hqq/eetq), 4 (bnb/hqq), 3 (hqq), 2 (hqq)] quantization_method: bnb # choices: [bnb, hqq, eetq] trust_remote_code: true ### method stage: sft do_train: true finetuning_type: lora lora_rank:8 lora_target:all### dataset dataset: coco-3000 template: qwen3_vl_nothink cutoff_len:2048# max_samples: 1000 preprocessing_num_workers:16 dataloader_num_workers:4### output output_dir: saves/qwen3-2b-coco-3000/lora/sft logging_steps:10 save_steps:500 plot_loss: true overwrite_output_dir: true save_only_model: false report_to: none # choices: [none, wandb, tensorboard, swanlab, mlflow]### train per_device_train_batch_size:2 gradient_accumulation_steps:4 learning_rate:1e-5 num_train_epochs:2 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### swanlab use_swanlab: true swanlab_project: llamafactory swanlab_run_name: Qwen3-VL-2B-Instruct-llamafactory

启动训练

uv run llamafactory-cli train examples/train_qlora/qwen3-coco.yaml 
➜ LlamaFactory git:(main) ✗ uv run llamafactory-cli train examples/train_qlora/qwen3-coco.yaml [WARNING|2026-02-0617:47:42] llamafactory.hparams.parser:148>> We recommend enable `upcast_layernorm` in quantized training. r Qwen3VLVideoProcessor {"crop_size": null,"data_format":"channels_first","default_to_square": true,"device": null,"do_center_crop": null,"do_convert_rgb": true,"do_normalize": true,"do_rescale": true,"do_resize": true,"do_sample_frames": true,"fps":2,"image_mean":[0.5,0.5,0.5],"image_std":[0.5,0.5,0.5],"input_data_format": null,"max_frames":768,"merge_size":2,"min_frames":4,"num_frames": null,"pad_size": null,"patch_size":16,"processor_class":"Qwen3VLProcessor","resample":3,"rescale_factor":0.00392156862745098,"return_metadata": false,"size":{"longest_edge":25165824,"shortest_edge":4096},"temporal_patch_size":2,"video_metadata": null,"video_processor_type":"Qwen3VLVideoProcessor"}[INFO|processing_utils.py:1116]2026-02-0617:47:50,292>> loading configuration file processor_config.json from cache at None[INFO|processing_utils.py:1199]2026-02-0617:47:50,543>> Processor Qwen3VLProcessor:- image_processor: Qwen2VLImageProcessorFast {"crop_size": null,"data_format":"channels_first","default_to_square": true,"device": null,"disable_grouping": null,"do_center_crop": null,"do_convert_rgb": true,"do_normalize": true,"do_pad": null,"do_rescale": true,"do_resize": true,"image_mean":[0.5,0.5,0.5],"image_processor_type":"Qwen2VLImageProcessorFast","image_std":[0.5,0.5,0.5],"input_data_format": null,"max_pixels": null,"merge_size":2,"min_pixels": null,"pad_size": null,"patch_size":16,"processor_class":"Qwen3VLProcessor","resample":3,"rescale_factor":0.00392156862745098,"return_tensors": null,"size":{"longest_edge":16777216,"shortest_edge":65536},"temporal_patch_size":2}- tokenizer: Qwen2TokenizerFast(name_or_path='Qwen/Qwen3-VL-2B-Instruct', vocab_size=151643, model_max_length=262144, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token':'<|im_end|>','pad_token':'<|endoftext|>','additional_special_tokens':['<|im_start|>','<|im_end|>','<|object_ref_start|>','<|object_ref_end|>','<|box_start|>','<|box_end|>','<|quad_start|>','<|quad_end|>','<|vision_start|>','<|vision_end|>','<|vision_pad|>','<|image_pad|>',[INFO|trainer.py:2519]2026-02-0617:47:58,649>>***** Running training *****[INFO|trainer.py:2520]2026-02-0617:47:58,649>> Num examples =600[INFO|trainer.py:2521]2026-02-0617:47:58,649>> Num Epochs =2[INFO|trainer.py:2522]2026-02-0617:47:58,649>> Instantaneous batch size per device =2[INFO|trainer.py:2525]2026-02-0617:47:58,649>> Total train batch size (w. parallel, distributed & accumulation)=8[INFO|trainer.py:2526]2026-02-0617:47:58,649>> Gradient Accumulation steps =4[INFO|trainer.py:2527]2026-02-0617:47:58,649>> Total optimization steps =150[INFO|trainer.py:2528]2026-02-0617:47:58,651>> Number of trainable parameters =8,716,288 swanlab: swanlab version 0.7.7is available! Upgrade: `pip install -U swanlab` swanlab: Tracking run with swanlab version 0.7.6 swanlab: Run data will be saved locally in/home/lijinkui/Desktop/tmp/TrainPlatform/LlamaFactory/swanlog/run-20260206_174759-8rc4pwadl4xmyy29n1bqg swanlab: 👋 Hi goldsunshine,welcome to swanlab! swanlab: Syncing run Qwen3-VL-2B-Instruct-llamafactory to the cloud swanlab: 🏠 View project at https://swanlab.cn/@goldsunshine/llamafactory swanlab: 🚀 View run at https://swanlab.cn/@goldsunshine/llamafactory/runs/8rc4pwadl4xmyy29n1bqg {'loss':4.3662,'grad_norm':5.828382968902588,'learning_rate':6e-06,'epoch':0.13}{'loss':4.389,'grad_norm':6.548262119293213,'learning_rate':9.978353953249023e-06,'epoch':0.27}{'loss':4.0005,'grad_norm':6.604191303253174,'learning_rate':9.736983212571646e-06,'epoch':0.4}{'loss':3.4562,'grad_norm':5.726210117340088,'learning_rate':9.24024048078213e-06,'epoch':0.53}{'loss':3.1868,'grad_norm':3.4086873531341553,'learning_rate':8.51490528712831e-06,'epoch':0.67}{'loss':2.9764,'grad_norm':2.1550605297088623,'learning_rate':7.600080639646077e-06,'epoch':0.8}{'loss':2.9609,'grad_norm':2.266796112060547,'learning_rate':6.545084971874738e-06,'epoch':0.93}{'loss':2.7471,'grad_norm':1.8668205738067627,'learning_rate':5.406793373339292e-06,'epoch':1.07}{'loss':2.9607,'grad_norm':2.0235414505004883,'learning_rate':4.246571438752585e-06,'epoch':1.2}{'loss':2.7321,'grad_norm':1.6290875673294067,'learning_rate':3.12696703292044e-06,'epoch':1.33}{'loss':2.6867,'grad_norm':2.1829676628112793,'learning_rate':2.1083383191600676e-06,'epoch':1.47}{'loss':2.7761,'grad_norm':1.8782838582992554,'learning_rate':1.2455998350925042e-06,'epoch':1.6}{'loss':2.6362,'grad_norm':1.8889576196670532,'learning_rate':5.852620357053651e-07,'epoch':1.73}{'loss':2.6991,'grad_norm':2.0048000812530518,'learning_rate':1.6292390268568103e-07,'epoch':1.87}{'loss':2.6784,'grad_norm':1.9924118518829346,'learning_rate':1.3537941026914302e-09,'epoch':2.0}100%|███████████████████████████████████████████████████████████████████████████|150/150[01:30<00:00,1.65it/s][INFO|trainer.py:4309]2026-02-0617:49:31,374>> Saving model checkpoint to saves/qwen3-2b-coco-3000/lora/sft/checkpoint-150{'train_runtime':93.701,'train_samples_per_second':12.807,'train_steps_per_second':1.601,'train_loss':3.1501645787556964,'epoch':2.0}100%|███████████████████████████████████████████████████████████████████████████|150/150[01:31<00:00,1.63it/s] epoch =2.0 total_flos = 1679346GF train_loss =3.1502 train_runtime =0:01:33.70 train_samples_per_second =12.807 train_steps_per_second =1.601 Figure saved at: saves/qwen3-2b-coco-3000/lora/sft/training_loss.png [WARNING|2026-02-0617:49:33] llamafactory.extras.ploting:148>> No metric eval_loss to plot.[WARNING|2026-02-0617:49:33] llamafactory.extras.ploting:148>> No metric eval_accuracy to plot.[INFO|modelcard.py:456]2026-02-0617:49:33,450>> Dropping the following result as it does not have all the necessary fields:{'task':{'name':'Causal Language Modeling','type':'text-generation'}} swanlab: Experiment Qwen3-VL-2B-Instruct-llamafactory has completed swanlab: 🏠 View project at https://swanlab.cn/@goldsunshine/llamafactory swanlab: 🚀 View run at https://swanlab.cn/@goldsunshine/llamafactory/runs/8rc4pwadl4xmyy29n1bqg

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