adapter: lora
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
bf16: true
chat_template: llama3
data_processes: 8
dataset_prepared_path: null
datasets:
- data_files:
  - 732414996d474f8f_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/732414996d474f8f_train_data.json
  type:
    field_instruction: context
    field_output: text
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 1
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: dimasik2987/2b32285c-ee33-46f2-8e3d-89d67f64898b
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 128
lora_dropout: 0.05
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: 80GiB
  cpu: 30GiB
max_steps: 150
micro_batch_size: 6
mlflow_experiment_name: /tmp/732414996d474f8f_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: 50
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: d22eabf3-5260-4b98-abcc-d9fc6f2c35ac
wandb_project: cold6
wandb_run: your_name
wandb_runid: d22eabf3-5260-4b98-abcc-d9fc6f2c35ac
warmup_steps: 10
weight_decay: 0.001
xformers_attention: null

2b32285c-ee33-46f2-8e3d-89d67f64898b

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5767

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-05
train_batch_size: 6
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 24
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: 150

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0001	1	1.2777
0.8642	0.0048	50	0.6573
0.6943	0.0097	100	0.6433
0.709	0.0145	150	0.5767

Framework versions

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