adapter: lora
base_model: openlm-research/open_llama_3b
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 3c8ed32d552069dc_train_data.json
  ds_type: json
  field: question
  path: /workspace/input_data/3c8ed32d552069dc_train_data.json
  type: completion
ddp_find_unused_parameters: false
distributed_type: ddp
early_stopping_patience: null
env:
  CUDA_VISIBLE_DEVICES: 0,1
  MASTER_ADDR: localhost
  MASTER_PORT: '29500'
  NCCL_DEBUG: INFO
  NCCL_IB_DISABLE: '0'
  NCCL_P2P_DISABLE: '0'
  NCCL_P2P_LEVEL: NVL
  PYTORCH_CUDA_ALLOC_CONF: max_split_size_mb:512, garbage_collection_threshold:0.8
  WORLD_SIZE: '2'
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: true
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: true
hub_model_id: fats-fme/7e11dd73-ab74-4d3c-859a-ab727ffa96bc
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory_MB: 65000
max_steps: -1
micro_batch_size: 2
mlflow_experiment_name: /tmp/3c8ed32d552069dc_train_data.json
model_type: AutoModelForCausalLM
num_devices: 2
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 2048
special_tokens:
  pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7e11dd73-ab74-4d3c-859a-ab727ffa96bc
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7e11dd73-ab74-4d3c-859a-ab727ffa96bc
warmup_steps: 50
world_size: 2
xformers_attention: true

7e11dd73-ab74-4d3c-859a-ab727ffa96bc

This model is a fine-tuned version of openlm-research/open_llama_3b on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7375

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: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
total_train_batch_size: 32
total_eval_batch_size: 4
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: 50
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.1387	0.0018	1	1.4083
0.8105	0.2504	141	0.8086
0.7853	0.5008	282	0.7659
0.6297	0.7512	423	0.7375

Framework versions

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