adapter: lora
base_model: tiiuae/falcon-rw-1b
bf16: auto
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
  - d9bf134d8c3d5800_train_data.json
  ds_type: json
  field: question
  path: /workspace/input_data/d9bf134d8c3d5800_train_data.json
  type: completion
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 8
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hub_model_id: nttx/c02917cc-8f87-4062-9fdc-c3e78cea4836
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 2.0
max_memory:
  0: 70GB
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/d9bf134d8c3d5800_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_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
special_tokens:
  pad_token: <|endoftext|>
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: c02917cc-8f87-4062-9fdc-c3e78cea4836
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c02917cc-8f87-4062-9fdc-c3e78cea4836
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null

c02917cc-8f87-4062-9fdc-c3e78cea4836

This model is a fine-tuned version of tiiuae/falcon-rw-1b on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.2274

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: 5e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
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: 30
training_steps: 100

Training results

Training Loss	Epoch	Step	Validation Loss
1.572	0.0001	1	2.3208
4.5455	0.0064	50	2.2739
4.3375	0.0128	100	2.2274

Framework versions

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