---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
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|>

```

</details><br>

# llama-3-8b-lora-law2entity

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/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