metadata
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-3B
tags:
- generated_from_trainer
model-index:
- name: outputs/dippy-2
results: []
See axolotl config
axolotl version: 0.5.0
base_model: meta-llama/Llama-3.2-3B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
#wget -O dataset_2000.jsonl http://94.130.230.31/dataset_2000.jsonl
chat_template: llama3
datasets:
- path: ./dataset_2000.jsonl
type: chat_template
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/dippy-2
sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
- embed_tokens
- lm_head
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 12
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16: true
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
outputs/dippy-2
This model is a fine-tuned version of meta-llama/Llama-3.2-3B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.0961
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
- num_epochs: 12
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9507 | 0.0153 | 1 | 1.9943 |
| 1.714 | 0.2605 | 17 | 1.7193 |
| 1.5507 | 0.5211 | 34 | 1.7040 |
| 1.6354 | 0.7816 | 51 | 1.6666 |
| 0.9188 | 1.0383 | 68 | 1.6559 |
| 0.8897 | 1.2989 | 85 | 1.6953 |
| 0.9014 | 1.5594 | 102 | 1.7119 |
| 0.8517 | 1.8199 | 119 | 1.7209 |
| 0.4448 | 2.0843 | 136 | 1.7969 |
| 0.4053 | 2.3448 | 153 | 1.8347 |
| 0.3723 | 2.6054 | 170 | 1.8777 |
| 0.339 | 2.8659 | 187 | 1.8751 |
| 0.1614 | 3.1264 | 204 | 2.0658 |
| 0.1804 | 3.3870 | 221 | 2.0643 |
| 0.1881 | 3.6475 | 238 | 2.0924 |
| 0.1762 | 3.9080 | 255 | 2.0624 |
| 0.195 | 4.1686 | 272 | 2.3268 |
| 0.0649 | 4.4291 | 289 | 2.2718 |
| 0.0786 | 4.6897 | 306 | 2.2569 |
| 0.0763 | 4.9502 | 323 | 2.2521 |
| 0.0509 | 5.2107 | 340 | 2.4546 |
| 0.0374 | 5.4713 | 357 | 2.4693 |
| 0.0216 | 5.7318 | 374 | 2.4763 |
| 0.0272 | 5.9923 | 391 | 2.5110 |
| 0.0117 | 6.2490 | 408 | 2.7330 |
| 0.0115 | 6.5096 | 425 | 2.6403 |
| 0.0092 | 6.7701 | 442 | 2.7747 |
| 0.0064 | 7.0268 | 459 | 2.7342 |
| 0.0059 | 7.2874 | 476 | 2.8930 |
| 0.0065 | 7.5479 | 493 | 2.9133 |
| 0.0059 | 7.8084 | 510 | 2.9216 |
| 0.0058 | 8.0690 | 527 | 2.9435 |
| 0.0046 | 8.3295 | 544 | 3.0068 |
| 0.0051 | 8.5900 | 561 | 3.0261 |
| 0.0044 | 8.8506 | 578 | 3.0278 |
| 0.0035 | 9.1073 | 595 | 3.0368 |
| 0.0038 | 9.3678 | 612 | 3.0577 |
| 0.004 | 9.6284 | 629 | 3.0710 |
| 0.0041 | 9.8889 | 646 | 3.0796 |
| 0.0038 | 10.1533 | 663 | 3.0823 |
| 0.0039 | 10.4138 | 680 | 3.0844 |
| 0.0041 | 10.6743 | 697 | 3.0886 |
| 0.004 | 10.9349 | 714 | 3.0952 |
| 0.0038 | 11.1992 | 731 | 3.0955 |
| 0.0033 | 11.4598 | 748 | 3.0949 |
| 0.0044 | 11.7203 | 765 | 3.0961 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3