adapter: lora
base_model: microsoft/Phi-3-mini-128k-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 03a02d4b38d228bc_train_data.json
  ds_type: json
  field: job_title
  path: /workspace/input_data/03a02d4b38d228bc_train_data.json
  type: completion
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 25
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: nttx/ef573d71-68f1-4f4b-b3d6-5cf8f8305d2e
hub_repo: null
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: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GB
max_steps: 50
micro_batch_size: 8
mlflow_experiment_name: /tmp/03a02d4b38d228bc_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
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
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: ef573d71-68f1-4f4b-b3d6-5cf8f8305d2e
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ef573d71-68f1-4f4b-b3d6-5cf8f8305d2e
warmup_steps: 20
weight_decay: 0.0
xformers_attention: null

ef573d71-68f1-4f4b-b3d6-5cf8f8305d2e

This model is a fine-tuned version of microsoft/Phi-3-mini-128k-instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7191

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss
29.1815	0.0092	1	6.9925
4.3471	0.2304	25	0.8878
2.5976	0.4608	50	0.7191

Framework versions

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