adapter: lora
base_model: microsoft/Phi-3.5-mini-instruct
bf16: auto
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 4
dataset_prepared_path: null
datasets:
- data_files:
  - e15e7c82f1cf2b70_train_data.json
  ds_type: json
  format: custom
  num_proc: 4
  path: /workspace/input_data/e15e7c82f1cf2b70_train_data.json
  streaming: true
  type:
    field_instruction: question
    field_output: final_answer
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map:
  lm_head: 3
  model.embed_tokens: 0
  model.layers.0: 0
  model.layers.1: 0
  model.layers.10: 3
  model.layers.11: 3
  model.layers.2: 0
  model.layers.3: 1
  model.layers.4: 1
  model.layers.5: 1
  model.layers.6: 2
  model.layers.7: 2
  model.layers.8: 2
  model.layers.9: 3
  model.norm: 3
do_eval: true
early_stopping_patience: 1
eval_batch_size: 1
eval_sample_packing: false
eval_steps: 25
evaluation_strategy: steps
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: true
hub_model_id: eeeebbb2/360e3670-d76d-4978-ae71-e28a5b339365
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 0.3
max_memory:
  0: 60GB
  1: 70GB
  2: 70GB
  3: 70GB
  cpu: 96GB
max_steps: 50
micro_batch_size: 1
mixed_precision: bf16
mlflow_experiment_name: /tmp/e15e7c82f1cf2b70_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: false
torch_dtype: bfloat16
train_on_inputs: false
trust_remote_code: true
use_cache: false
val_set_size: 50
wandb_entity: null
wandb_mode: online
wandb_name: 360e3670-d76d-4978-ae71-e28a5b339365
wandb_project: Public_TuningSN
wandb_runid: 360e3670-d76d-4978-ae71-e28a5b339365
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: null

360e3670-d76d-4978-ae71-e28a5b339365

This model is a fine-tuned version of microsoft/Phi-3.5-mini-instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6944

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: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 32
total_train_batch_size: 128
total_eval_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 2
training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss
30.6843	0.0046	1	1.4357
15.6541	0.1139	25	0.7260
15.0867	0.2279	50	0.6944

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1