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|2026-02-06 17:47:42] 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] 2026-02-06 17:47:50,292 >> loading configuration file processor_config.json from cache at None
[INFO|processing_utils.py:1199] 2026-02-06 17:47:50,543 >> 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] 2026-02-06 17:47:58,649 >> ***** Running training *****
[INFO|trainer.py:2520] 2026-02-06 17:47:58,649 >>   Num examples = 600
[INFO|trainer.py:2521] 2026-02-06 17:47:58,649 >>   Num Epochs = 2
[INFO|trainer.py:2522] 2026-02-06 17:47:58,649 >>   Instantaneous batch size per device = 2
[INFO|trainer.py:2525] 2026-02-06 17:47:58,649 >>   Total train batch size (w. parallel, distributed & accumulation) = 8
[INFO|trainer.py:2526] 2026-02-06 17:47:58,649 >>   Gradient Accumulation steps = 4
[INFO|trainer.py:2527] 2026-02-06 17:47:58,649 >>   Total optimization steps = 150
[INFO|trainer.py:2528] 2026-02-06 17:47:58,651 >>   Number of trainable parameters = 8,716,288
swanlab: swanlab version 0.7.7 is 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] 2026-02-06 17:49:31,374 >> 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|2026-02-06 17:49:33] llamafactory.extras.ploting:148 >> No metric eval_loss to plot.
[WARNING|2026-02-06 17:49:33] llamafactory.extras.ploting:148 >> No metric eval_accuracy to plot.
[INFO|modelcard.py:456] 2026-02-06 17:49:33,450 >> 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

训练过程记录

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