See axolotl config
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|>"
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
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.6022 | 0.0202 | 1 | 1.5845 |
1.2173 | 0.9899 | 49 | 1.1328 |
0.9676 | 1.9798 | 98 | 1.1247 |
Framework versions
- PEFT 0.12.0
- Transformers 4.45.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for winglian/llama-3.1-8b-talk-like-a-pirate
Base model
NousResearch/Meta-Llama-3.1-8B