See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: openlm-research/open_llama_3b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - ce7c891b2dc5fc49_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/ce7c891b2dc5fc49_train_data.json
  type:
    field_input: ''
    field_instruction: instruction
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
ddp_find_unused_parameters: false
distributed_type: ddp
early_stopping_patience: null
env:
  CUDA_VISIBLE_DEVICES: 0,1
  MASTER_ADDR: localhost
  MASTER_PORT: '29500'
  NCCL_DEBUG: INFO
  NCCL_IB_DISABLE: '0'
  NCCL_P2P_DISABLE: '0'
  NCCL_P2P_LEVEL: NVL
  PYTORCH_CUDA_ALLOC_CONF: max_split_size_mb:512, garbage_collection_threshold:0.8
  WORLD_SIZE: '2'
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: fats-fme/1d5b1e3b-23c5-4f82-902f-46f6460074a2
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: true
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_MB: 65000
max_steps: -1
micro_batch_size: 1
mlflow_experiment_name: /tmp/ce7c891b2dc5fc49_train_data.json
model_type: AutoModelForCausalLM
num_devices: 2
num_epochs: 1
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: </s>
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: 1d5b1e3b-23c5-4f82-902f-46f6460074a2
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1d5b1e3b-23c5-4f82-902f-46f6460074a2
warmup_steps: 50
world_size: 2
xformers_attention: true

1d5b1e3b-23c5-4f82-902f-46f6460074a2

This model is a fine-tuned version of openlm-research/open_llama_3b on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0284

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: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• total_eval_batch_size: 2
• 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: 50
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.8308	0.0003	1	1.5086
1.0443	0.2502	766	1.0733
0.8601	0.5004	1532	1.0430
1.4479	0.7506	2298	1.0284

Framework versions

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