See axolotl config

axolotl version: 0.5.3.dev0

wandb_project: llm-training-platform
wandb_name: II-Tulu-3B-SFT
datasets:
- path: allenai/tulu-3-sft-mixture
  split: train
  type: chat_template
  field_messages: messages
  message_field_role: role
  message_field_content: content
  roles:
    system:
    - system
    user:
    - user
    assistant:
    - assistant
chat_template: qwen_25
sequence_len: 2048
base_model: Qwen/Qwen2.5-3B
output_dir: checkpoints/1357e2cd-76bc-46d5-a394-949b712427c7
dataset_prepared_path: checkpoints/1357e2cd-76bc-46d5-a394-949b712427c7/dataset_prepared
flash_attention: true
train_on_inputs: false
pad_to_sequence_len: true
eval_sample_packing: false
push_to_hub: true
bf16: auto
gradient_checkpointing: true
logging_steps: 10
hub_model_id: phunguyen01/II-Tulu-3B-SFT
learning_rate: 5.0e-06
micro_batch_size: 8
num_epochs: 2
seed: 42
gradient_accumulation_steps: 2
sample_packing: true
val_set_size: 0

II-Tulu-3B-SFT

This model is a fine-tuned version of Qwen/Qwen2.5-3B on the allenai/tulu-3-sft-mixture dataset.

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-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 2
• total_train_batch_size: 128
• total_eval_batch_size: 64
• optimizer: Use adamw_hf 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: 100
• num_epochs: 2

Training results

Framework versions

• Transformers 4.47.0
• Pytorch 2.4.0+cu121
• Datasets 3.1.0
• Tokenizers 0.21.0