llama-3.1-8b-talk-like-a-pirate

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.1

base_model: NousResearch/Meta-Llama-3.1-8B

load_in_4bit: true
strict: false

chat_template: llama3
datasets:
  - path: winglian/pirate-ultrachat-10k
    type: chat_template
    message_field_role: role
    message_field_content: content
dataset_prepared_path: last_run_prepared
val_set_size: 0.005
output_dir: ./outputs/lora-out

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

adapter: qlora
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_modules_to_save:
  - embed_tokens
  - lm_head
peft_use_dora: true

wandb_project: pirate-ultrachat-llama31
wandb_entity: axolotl-ai

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
bf16: true
tf32: true

gradient_checkpointing: true
logging_steps: 1
flash_attention: true

warmup_ration: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
weight_decay: 0.0
deepspeed: deepspeed_configs/zero2.json
special_tokens:
  pad_token: "<|finetune_right_pad_id|>"

</details><br>

outputs/lora-out

This model is a fine-tuned version of NousResearch/Meta-Llama-3.1-8B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.1247

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: 0.0002
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• total_eval_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 2
• num_epochs: 2

Training results

Framework versions

• PEFT 0.12.0
• Transformers 4.45.0.dev0
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1