meta-llama/Llama-4-Scout-17B-16E-Instruct · Llama 4

LlamaFactory now supports fine-tuning the Llama 4 herd of models using FFT & LoRA training, we provide training and inference demo along with our no-code Web UI!

Install

# build on this commit
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[torch,metrics,deepspeed]"
pip install git+https://github.com/hiyouga/transformers.git@llama4_train

These tests were done with the LoRA method and text & multimodal interleaved data setups on 8 * L20 48G GPUs.

Test recipes

### model
model_name_or_path: meta-llama/Llama-4-Scout-17B-16E-Instruct
trust_remote_code: true

### method
stage: sft
do_train: true
finetuning_type: lora
lora_rank: 8
lora_target: all
deepspeed: examples/deepspeed/ds_z3_config.json  # choices: [ds_z0_config.json, ds_z2_config.json, ds_z3_config.json]

### dataset
dataset: mllm_demo,identity,alpaca_en_demo
template: llama4
cutoff_len: 2048
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
dataloader_num_workers: 4

### output
output_dir: saves/llama4-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
save_only_model: false

### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 2
learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
resume_from_checkpoint: null

### eval
# eval_dataset: alpaca_en_demo
# val_size: 0.1
# per_device_eval_batch_size: 1
# eval_strategy: steps
# eval_steps: 500

Reproducible recipe 🤗 https://github.com/hiyouga/LLaMA-Factory/pull/7611

meta-llama
/

Llama-4-Scout-17B-16E-Instruct

Llama 4 - open-source fine-tuning script

Install

Test recipes