---
license: llama3
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
model-index:
- name: isafpr-llama3-lora-templatefree
  results: []
---

<!-- 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: false
load_in_4bit: true
strict: false

data_seed: 42
seed: 42

datasets:
  - path: data/templatefree_isaf_press_releases_ft_train.jsonl
    type: input_output
dataset_prepared_path:
val_set_size: 0.1
output_dir: ./outputs/llama3/lora-out-templatefree
hub_model_id: strickvl/isafpr-llama3-lora-templatefree

sequence_len: 1024
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: isaf_pr_ft
wandb_entity: strickvl
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:
  bos_token: "<s>"
  eos_token: "</s>"
  pad_token: <|end_of_text|>

```

</details><br>

# isafpr-llama3-lora-templatefree

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0428

## 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: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.1433        | 0.0071 | 1    | 2.1450          |
| 0.0712        | 0.25   | 35   | 0.0669          |
| 0.0549        | 0.5    | 70   | 0.0517          |
| 0.0585        | 0.75   | 105  | 0.0479          |
| 0.0452        | 1.0    | 140  | 0.0482          |
| 0.0244        | 1.2339 | 175  | 0.0473          |
| 0.0287        | 1.4839 | 210  | 0.0447          |
| 0.017         | 1.7339 | 245  | 0.0417          |
| 0.0107        | 1.9839 | 280  | 0.0408          |
| 0.0151        | 2.2143 | 315  | 0.0414          |
| 0.0134        | 2.4643 | 350  | 0.0415          |
| 0.0067        | 2.7143 | 385  | 0.0407          |
| 0.0089        | 2.9643 | 420  | 0.0399          |
| 0.0092        | 3.1929 | 455  | 0.0421          |
| 0.007         | 3.4429 | 490  | 0.0429          |
| 0.0065        | 3.6929 | 525  | 0.0428          |
| 0.0125        | 3.9429 | 560  | 0.0428          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1