See axolotl config
axolotl version: 0.4.0
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: taozi555/bagel
type: sharegpt
# - path: jondurbin/cinematika-v0.1
# type: text
- path: MinervaAI/Aesir-Preview
type: sharegpt
- path: Norquinal/claude_multiround_chat_30k
type: sharegpt
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./out
chat_template: alpaca
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
wandb_project: waifu-8b
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 2
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
eval_steps: 100
eval_table_size:
saves_per_epoch:
save_steps: 100
save_total_limit: 20
debug:
deepspeed: /workspace/deepspeed.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
out
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7773
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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.0419 | 0.0 | 1 | 1.1113 |
0.9179 | 0.07 | 100 | 0.8886 |
1.0123 | 0.14 | 200 | 0.8822 |
0.9106 | 0.21 | 300 | 0.8701 |
0.8992 | 0.28 | 400 | 0.8637 |
0.7915 | 0.35 | 500 | 0.8527 |
0.9123 | 0.42 | 600 | 0.8448 |
0.7849 | 0.49 | 700 | 0.8381 |
0.8381 | 0.56 | 800 | 0.8344 |
0.7652 | 0.63 | 900 | 0.8230 |
0.9006 | 0.7 | 1000 | 0.8167 |
0.8589 | 0.77 | 1100 | 0.8088 |
0.7635 | 0.84 | 1200 | 0.8016 |
0.7696 | 0.91 | 1300 | 0.7951 |
0.8476 | 0.98 | 1400 | 0.7879 |
0.6031 | 1.03 | 1500 | 0.8063 |
0.5386 | 1.09 | 1600 | 0.8065 |
0.5298 | 1.16 | 1700 | 0.8015 |
0.5736 | 1.23 | 1800 | 0.7979 |
0.5761 | 1.3 | 1900 | 0.7939 |
0.5576 | 1.37 | 2000 | 0.7917 |
0.4814 | 1.44 | 2100 | 0.7879 |
0.5146 | 1.51 | 2200 | 0.7842 |
0.4577 | 1.58 | 2300 | 0.7832 |
0.4821 | 1.65 | 2400 | 0.7806 |
0.6088 | 1.72 | 2500 | 0.7782 |
0.5113 | 1.79 | 2600 | 0.7785 |
0.5861 | 1.86 | 2700 | 0.7779 |
0.4885 | 1.93 | 2800 | 0.7773 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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meta-llama/Meta-Llama-3-8B
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