anastas5/llama3.1-8B-Instruct-rus-test-v3

See axolotl config

axolotl version: 0.4.1

base_model: anastas5/llama3.1-8B-Instruct-rus-test-v2

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: anastas5/dataset-rus-test-six
    type: sharegpt
    conversation: llama-3
dataset_prepared_path: /home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/prepared_tagengo_rus
val_set_size: 0.05
output_dir: /home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/output_llama3_8b_gpt_4o_ru

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

use_wandb: false

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-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: 10
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
weight_decay: 0.0
special_tokens:
  pad_token: <|end_of_text|>

home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/output_llama3_8b_gpt_4o_ru

This model is a fine-tuned version of anastas5/llama3.1-8B-Instruct-rus-test-v2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7729

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: 1e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
0.957	0.0769	1	0.8838
0.9398	0.2308	3	0.8762
1.0969	0.4615	6	0.8373
0.9608	0.6923	9	0.8226
0.8364	0.9231	12	0.8146
0.7566	1.1154	15	0.7914
0.7927	1.3462	18	0.7818
0.74	1.5769	21	0.7784
0.7247	1.8077	24	0.7783
0.7261	2.0385	27	0.7748
0.7255	2.2308	30	0.7727
0.6439	2.4615	33	0.7726
0.5354	2.6923	36	0.7725
0.638	2.9231	39	0.7734
0.6246	3.0769	42	0.7726
0.5374	3.3077	45	0.7726
0.5539	3.5385	48	0.7729
0.6056	3.7692	51	0.7729

Framework versions

• Transformers 4.45.1
• Pytorch 2.3.1+cu121
• Datasets 3.0.1
• Tokenizers 0.20.1