NA94/Clarky

See axolotl config

axolotl version: 0.8.0.dev0

base_model: NousResearch/Meta-Llama-3.1-8B
# Model loading settings
load_in_8bit: false
load_in_4bit: false
strict: false
# Dataset configuration
datasets:
  - path: NA94/clarky_alpaca
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/out
# Training parameters
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
# Weights & Biases logging (optional)
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
# Training optimization
gradient_accumulation_steps: 8
micro_batch_size: 2
max_steps: 100
warmup_steps: 5
eval_steps: 10
logging_steps: 5
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 5e-5
# Additional settings
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
    use_reentrant: false
#early_stopping_patience:
resume_from_checkpoint:
#logging_steps: 1
#xformers_attention:
flash_attention: true
eval_sample_packing: false
#evals_per_epoch: 2
#eval_table_size:
#saves_per_epoch: 1
#debug:
#deepspeed:
weight_decay: 0.0
#fsdp:
#fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

outputs/out

This model is a fine-tuned version of NousResearch/Meta-Llama-3.1-8B on the NA94/clarky_alpaca 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-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 5
• training_steps: 0

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	1	3.8304

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0