adapter: lora
base_model: fxmarty/tiny-llama-fast-tokenizer
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - eaa2bf0a263ce91a_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/eaa2bf0a263ce91a_train_data.json
  type:
    field_input: prompt_list
    field_instruction: prompt
    field_output: summary
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: null
eval_max_new_tokens: 256
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: ivangrapher/4e01c9fc-e817-48c8-b51d-c45ae1c55748
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 75GiB
max_steps: 40
micro_batch_size: 2
mlflow_experiment_name: /tmp/eaa2bf0a263ce91a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
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: 20
sequence_len: 1024
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: d47af639-89d3-4eaf-9b1b-0ec3d6b512ef
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d47af639-89d3-4eaf-9b1b-0ec3d6b512ef
warmup_steps: 20
weight_decay: 0.01
xformers_attention: true

4e01c9fc-e817-48c8-b51d-c45ae1c55748

This model is a fine-tuned version of fxmarty/tiny-llama-fast-tokenizer on the None dataset. It achieves the following results on the evaluation set:

Loss: 10.3749

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: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
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: 20
training_steps: 40

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0008	1	10.3781
10.3772	0.0042	5	10.3780
10.3764	0.0085	10	10.3777
10.3767	0.0127	15	10.3773
10.3773	0.0169	20	10.3767
10.3764	0.0212	25	10.3760
10.375	0.0254	30	10.3754
10.3758	0.0297	35	10.3750
10.3737	0.0339	40	10.3749

Framework versions

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