adapter: lora
base_model: numind/NuExtract-v1.5
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 073b335db390ceb8_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/073b335db390ceb8_train_data.json
  type:
    field_input: chunk
    field_instruction: title
    field_output: summary
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: nttx/9265d058-ea62-41d3-85cf-e4ff661b859f
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: 10
lora_alpha: 64
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 450
micro_batch_size: 8
mlflow_experiment_name: /tmp/073b335db390ceb8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
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: 29075f76-5293-45d3-bab3-d5ff3e7c16ed
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 29075f76-5293-45d3-bab3-d5ff3e7c16ed
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

9265d058-ea62-41d3-85cf-e4ff661b859f

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: 0.3857

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: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
training_steps: 450

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0008	1	2.1481
7.5913	0.0407	50	1.7043
5.9995	0.0814	100	1.3333
4.5399	0.1221	150	1.0348
3.9213	0.1628	200	0.7999
2.8768	0.2035	250	0.6270
2.1518	0.2442	300	0.5153
2.1031	0.2848	350	0.4349
1.9531	0.3255	400	0.3948
1.6952	0.3662	450	0.3857

Framework versions

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