See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: llamafactory/tiny-random-Llama-3
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - d7060b8372ad8ed2_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/d7060b8372ad8ed2_train_data.json
  type:
    field_input: source
    field_instruction: article_text
    field_output: title
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: mamung/eb61a1f7-a750-4dfc-8c2a-76e9388d210d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.00015
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 2
max_steps: 100
micro_batch_size: 2
mlflow_experiment_name: /tmp/d7060b8372ad8ed2_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1.0e-05
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 2048
special_tokens:
  pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: eddysang
wandb_mode: online
wandb_name: 77a8cf9b-bbe0-45ad-9e45-fc297aa43e47
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 77a8cf9b-bbe0-45ad-9e45-fc297aa43e47
warmup_steps: 20
weight_decay: 0.01
xformers_attention: false

eb61a1f7-a750-4dfc-8c2a-76e9388d210d

This model is a fine-tuned version of llamafactory/tiny-random-Llama-3 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 11.7448

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: 0.00015
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 32
• total_train_batch_size: 64
• 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-05
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 20
• training_steps: 100

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0008	1	11.7658
11.765	0.0075	9	11.7656
11.7631	0.0150	18	11.7646
11.7617	0.0226	27	11.7622
11.758	0.0301	36	11.7577
11.7549	0.0376	45	11.7516
11.748	0.0451	54	11.7475
11.7472	0.0527	63	11.7459
11.7475	0.0602	72	11.7452
11.7438	0.0677	81	11.7449
11.7479	0.0752	90	11.7448
11.7459	0.0827	99	11.7448

Framework versions

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