base_model: WizardLM/WizardCoder-3B-V1.0
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

hub_model_id: AlekseyKorshuk/WizardCoder-3B-V1.0-dpo-beta-0.01
hub_strategy: every_save

load_in_8bit: false
load_in_4bit: false
strict: false

rl: dpo
datasets:
  - path: AlekseyKorshuk/evol-codealpaca-v1-dpo
    split: train
    type: wizardcoder.intel
dataset_prepared_path: last_run_prepared

#val_set_size: 0.001
output_dir: ./output

sequence_len: 2048
#sample_packing: false  # currently unsupported
pad_to_sequence_len:

lora_r:
lora_alpha:
lora_dropout:
lora_target_modules:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: ui-thesis
wandb_entity:
wandb_watch:
wandb_name: WizardCoder-3B-V1.0-dpo-beta-0.01
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 8
num_epochs: 1
optimizer: paged_adamw_8bit
adam_beta1: 0.9
adam_beta2: 0.95
max_grad_norm: 1.0
adam_epsilon: 0.00001
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
learning_rate: 8.0e-7
warmup_steps: 32
#warmup_ratio: 0.1
weight_decay: 0.01
dpo_beta: 0.01

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true
#float16: false
#bfloat16: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false


#evals_per_epoch: 5
#eval_table_size: 8 # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
#eval_table_max_new_tokens: 768 # Total number of tokens generated for predictions sent to wandb. Default is 128

#chat_template: chatml
#saves_per_epoch: 1
save_steps: 500
save_total_limit: 1
seed: 42
debug:
deepspeed:


fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true

WizardCoder-3B-V1.0-dpo-beta-0.01

This model is a fine-tuned version of WizardLM/WizardCoder-3B-V1.0 on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 8e-07
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 32
training_steps: 312

Training results

Framework versions

Transformers 4.38.0.dev0
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0