base_model: TheBloke/Llama-2-7B-GPTQ
# optionally might have model_type or tokenizer_type
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

gptq: true
gptq_disable_exllama: true

tokenizer_use_fast: true
tokenizer_legacy: true
push_dataset_to_hub:
hf_use_auth_token: true
datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
adapter: lora
lora_model_dir:
sequence_len: 4096
sample_packing:
lora_r: 8
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - k_proj
  - o_proj
  - q_proj
  - v_proj
lora_target_linear:
wandb_project:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./outputs/model-out
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_torch_fused
adam_beta2: 0.95
adam_eps: 0.00001
max_grad_norm: 1.0
torchdistx_path:
lr_scheduler: cosine
lr_quadratic_warmup: true
learning_rate: 0.000017
bf16: false
fp16: false
float16: true
tf32: true
gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention:
sdp_attention:
flash_optimum:
warmup_steps: 100
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.1
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"