adapter: lora
auto_resume_from_checkpoints: false
base_model: unsloth/tinyllama
bf16: auto
chat_template: llama3
dataset_prepared_path: null
dataset_processes: 6
datasets:
- data_files:
  - f52b5c4411fd447c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/f52b5c4411fd447c_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 200
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: false
hub_model_id: error577/ed864f34-7a77-4fe8-98e8-0903340e71dd
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.00000002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.0
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_steps: null
micro_batch_size: 8
mlflow_experiment_name: /tmp/f52b5c4411fd447c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 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: 200
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.005
wandb_entity: null
wandb_mode: online
wandb_name: b32afb04-01f6-473a-a1bd-85989cd2c8d6
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b32afb04-01f6-473a-a1bd-85989cd2c8d6
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null

ed864f34-7a77-4fe8-98e8-0903340e71dd

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

Loss: 1.9212

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: 2e-08
train_batch_size: 8
eval_batch_size: 8
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=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 30
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
1.9788	0.0006	1	1.9194
1.9983	0.1223	200	1.9197
1.8519	0.2446	400	1.9200
1.9556	0.3669	600	1.9202
1.8386	0.4891	800	1.9212

Framework versions

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