/examples/v1/train_qlora/quantization.yaml

model: Qwen/Qwen3-0.6B
model_class: llm


# PEFT Configuration
peft_config:
  name: lora
  r: 16
  lora_alpha: 32
  lora_dropout: 0.05
  target_modules: all

# Kernel Config
kernel_config:
  name: auto
  include_kernels: auto

# FSDP Config
dist_config:
  name: fsdp2
  dcp_path: null

# Quantization Config
quant_config:
  name: bnb # choice: auto/bnb if auto is selected, the quantization method will be automatically selected based on the model and environment.
  quantization_bit: 4 # choice: 8/4(bnb)

### data
train_dataset: data/v1_sft_demo.yaml

### training
output_dir: outputs/test_quantization
micro_batch_size: 1
cutoff_len: 2048
learning_rate: 1.0e-4
max_steps: 10

### sample
sample_backend: hf
max_new_tokens: 128