See axolotl config

axolotl version: 0.6.0

base_model: NousResearch/Meta-Llama-3.1-8B
# Automatically upload checkpoint and final model to HF
hub_model_id: Siguiente-ia/PLEX-0.1-8b

load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: chatml

datasets:
  - path: Siguiente-ia/plex-v0.2
    type: chat_template
    field_messages: messages
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/out

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

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

warmup_steps: 100
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|>

PLEX-0.1-8b

This model is a fine-tuned version of NousResearch/Meta-Llama-3.1-8B on the Siguiente-ia/plex-v0.2 dataset. It achieves the following results on the evaluation set:

• Loss: 0.6582

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: 2e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• 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: 100
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.8808	0.0019	1	0.8060
0.6044	0.5003	269	0.6582

Framework versions

• Transformers 4.47.1
• Pytorch 2.3.1+cu121
• Datasets 3.1.0
• Tokenizers 0.21.0