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axolotl/examples/llama-4/scout-qlora-single-h100.yaml

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YAML

base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
model_type: Llama4ForConditionalGeneration
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
strict: false
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_glu_activation: true
liger_rms_norm: true
liger_layer_norm: true
llama4_linearized_experts: true
load_in_4bit: true
adapter: qlora
lora_r: 32
lora_alpha: 64
lora_target_modules:
- self_attn.q_proj
- self_attn.k_proj
- self_attn.v_proj
- self_attn.o_proj
- shared_expert.gate_proj
- shared_expert.up_proj
- shared_expert.down_proj
# - experts.gate_projs.[0-9]+$
# - experts.up_projs.[0-9]+$
# - experts.down_projs.[0-9]+$
lora_modules_to_save:
# - lm_head
# - embed_tokens
lora_mlp_kernel: true
lora_qkv_kernel: true
lora_o_kernel: true
chat_template: llama4
datasets:
- path: mlabonne/FineTome-100k
type: chat_template
split: train[:20%]
field_messages: conversations
message_property_mappings:
role: from
content: value
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out
sequence_len: 4096 # up to 8k will work on a single H100
sample_packing: true
pad_to_sequence_len: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch_4bit
lr_scheduler: cosine
learning_rate: 1e-4
bf16: true
tf32: true
logging_steps: 1
flash_attention: true
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
use_reentrant: false
warmup_steps: 20
evals_per_epoch: 1
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
pad_token: <|finetune_right_pad_id|>
eos_token: <|eot|>