---
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: []
---

<!-- 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.5.0`
```yaml
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|>

```

</details><br>

# outputs/dippy-2

This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/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