base_model: 01-ai/Yi-34B-Chat
# optionally might have model_type or tokenizer_type
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

load_in_8bit: false
load_in_4bit: true
sequence_len: 1024
bf16: auto
tf32: false
flash_attention: true
special_tokens:
  bos_token: "<|startoftext|>"
  eos_token: "<|endoftext|>"
  unk_token: "<unk>"

# Data
datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
warmup_steps: 10

# Iterations
num_epochs: 1

# Evaluation
val_set_size: 0.1
evals_per_epoch: 5
eval_sample_packing: false
eval_batch_size: 1

# LoRA
output_dir: ./outputs/qlora-out
adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_target_modules:

# Sampling
sample_packing: false
pad_to_sequence_len: false

# Batching
gradient_accumulation_steps: 4
micro_batch_size: 1
gradient_checkpointing: true

# wandb
wandb_project:

# Optimizer
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

# Misc
resume_from_checkpoint:
logging_steps: 1
weight_decay: 0