license: llama3 library_name: peft tags: - axolotl - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B model-index: - name: llama-3-8b-lora-law2entity results: [] datasets: - rubenamtz0/law_entity_recognition
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: rubenamtz0/law_entity_recognition
type: alpaca
dataset_prepared_path:
val_set_size: 0.1
output_dir: ./outputs/lora-law
hub_model_id: rubenamtz0/llama-3-8b-lora-law2entity
sequence_len: 4096
sample_packing: true
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:
wandb_project: entity-relationship-claim-ft
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
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|>
llama-3-8b-lora-law2entity
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the rubenamtz0/law_entity_recognition dataset. It achieves the following results on the evaluation set: - Loss: 0.1490
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: 3 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - total_eval_batch_size: 6 - 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 |
|---|---|---|---|
| 0.2735 | 0.05 | 1 | 0.2923 |
| 0.2852 | 0.25 | 5 | 0.2742 |
| 0.2007 | 0.5 | 10 | 0.2015 |
| 0.1742 | 0.75 | 15 | 0.1807 |
| 0.1854 | 1.0 | 20 | 0.1688 |
| 0.159 | 1.1125 | 25 | 0.1630 |
| 0.1444 | 1.3625 | 30 | 0.1592 |
| 0.1479 | 1.6125 | 35 | 0.1565 |
| 0.1505 | 1.8625 | 40 | 0.1538 |
| 0.1369 | 2.1125 | 45 | 0.1518 |
| 0.1348 | 2.2125 | 50 | 0.1512 |
| 0.1287 | 2.4625 | 55 | 0.1510 |
| 0.1359 | 2.7125 | 60 | 0.1498 |
| 0.1367 | 2.9625 | 65 | 0.1491 |
| 0.1218 | 3.075 | 70 | 0.1491 |
| 0.1285 | 3.325 | 75 | 0.1493 |
| 0.1307 | 3.575 | 80 | 0.1490 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1