See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: HuggingFaceH4/tiny-random-LlamaForCausalLM
bf16: true
chat_template: llama3
datasets:
- data_files:
  - 801d20e7a2e7ce95_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/801d20e7a2e7ce95_train_data.json
  type:
    field_input: source
    field_instruction: cwe_id
    field_output: target
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: sn5601/e3ad9e95-04bd-431d-bbd9-2dcc8d91442f
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: 1
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: 77GiB
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/801d20e7a2e7ce95_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
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: 25
save_strategy: steps
sequence_len: 1024
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: sn56-miner
wandb_mode: disabled
wandb_name: e3ad9e95-04bd-431d-bbd9-2dcc8d91442f
wandb_project: god
wandb_run: your_name
wandb_runid: e3ad9e95-04bd-431d-bbd9-2dcc8d91442f
warmup_steps: 10
weight_decay: 0.01
xformers_attention: false

e3ad9e95-04bd-431d-bbd9-2dcc8d91442f

This model is a fine-tuned version of HuggingFaceH4/tiny-random-LlamaForCausalLM on the None dataset. It achieves the following results on the evaluation set:

• Loss: 10.3644

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: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• total_eval_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 10
• training_steps: 100

Training results

Training Loss	Epoch	Step	Validation Loss
10.3889	0.0081	1	10.3897
10.3866	0.0732	9	10.3885
10.3843	0.1463	18	10.3854
10.3801	0.2195	27	10.3822
10.3768	0.2927	36	10.3787
10.3737	0.3659	45	10.3750
10.3715	0.4390	54	10.3715
10.3674	0.5122	63	10.3684
10.3665	0.5854	72	10.3663
10.363	0.6585	81	10.3651
10.3636	0.7317	90	10.3645
10.3624	0.8049	99	10.3644

Framework versions

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