base_model: AlekseyKorshuk/ultrachat-phi-2-sft-chatml
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

hub_model_id: AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml
hub_strategy: every_save

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: AlekseyKorshuk/evol-codealpaca-v1-sft
    type: sharegpt
    conversation: chatml

dataset_prepared_path:
val_set_size: 0
output_dir: ./output

sequence_len: 2048
sample_packing: false
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: ultrachat-evolcode-phi-2-sft-chatml
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 16
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: 2e-5
warmup_ratio: 0.1
weight_decay: 0.1

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true

#bf16: false
#fp16: false
#tf32: false
#float16: true

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


evals_per_epoch: 0
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
eval_sample_packing: false

chat_template: chatml
saves_per_epoch: 5
save_total_limit: 1
seed: 42
debug:
deepspeed:

fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true

ultrachat-evolcode-phi-2-sft-chatml

This model is a fine-tuned version of AlekseyKorshuk/ultrachat-phi-2-sft-chatml 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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 7
num_epochs: 1

Training results

Framework versions

Transformers 4.37.0
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0