See axolotl config

axolotl version: 0.7.0

base_model: meta-llama/Llama-3.1-8B-Instruct
# optionally might have model_type or tokenizer_type
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: rtahmasbi/data_ex2_50
    type: alpaca
dataset_prepared_path:
val_set_size: 0
output_dir: ./outputs/data_ex2_50_v3

adapter: lora
lora_model_dir:

sequence_len: 15000
sample_packing: true
pad_to_sequence_len: true

lora_r: 16
lora_alpha: 32
lora_dropout: 0.0
lora_target_modules:
  - q_proj
  - v_proj
  - o_proj
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 30
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 3e-4

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: "<|end_of_text|>"

outputs/data_ex2_50_v3

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the rtahmasbi/data_ex2_50 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: 0.0003
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• 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: 100
• num_epochs: 30.0

Training results

Framework versions

• PEFT 0.14.0
• Transformers 4.48.3
• Pytorch 2.4.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0