adapter: lora
base_model: numind/NuExtract-v1.5
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - ec86aaf7a4d9d1f1_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/ec86aaf7a4d9d1f1_train_data.json
  type:
    field_instruction: instruction
    field_output: response
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 8
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: nttx/431c68eb-10c2-493e-a49c-b1615c3e10a3
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 3e-5
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 1500
micro_batch_size: 8
mlflow_experiment_name: /tmp/ec86aaf7a4d9d1f1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 15
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.999
  adam_epsilon: 1e-8
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: false
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 150
saves_per_epoch: null
sequence_len: 1024
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: d559828d-ba7d-4079-9d28-dc77b21f6d64
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d559828d-ba7d-4079-9d28-dc77b21f6d64
warmup_steps: 50
weight_decay: 0.1
xformers_attention: null

431c68eb-10c2-493e-a49c-b1615c3e10a3

This model is a fine-tuned version of numind/NuExtract-v1.5 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8570

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
training_steps: 1500

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0008	1	1.4816
1.9502	0.1194	150	1.0248
1.7991	0.2389	300	0.9619
1.6773	0.3583	450	0.9296
1.6092	0.4777	600	0.9065
1.5817	0.5971	750	0.8874
1.6725	0.7166	900	0.8753
1.5602	0.8360	1050	0.8662
1.6401	0.9554	1200	0.8596
1.6681	1.0748	1350	0.8578
1.6107	1.1943	1500	0.8570

Framework versions

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