---
base_model: meta-llama/Llama-3.2-3B
library_name: peft
license: llama3.2
tags:
- generated_from_trainer
model-index:
- name: model-out
  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: `0.4.1`
```yaml
base_model: meta-llama/Llama-3.2-3B

load_in_8bit: false
load_in_4bit: true
strict: false
adapter: qlora

# Data config
dataset_prepared_path: data
chat_template: llama3
datasets:
  - path: data/train.jsonl
    ds_type: json
    data_files:
      - data/train.jsonl
    type: sharegpt

test_datasets:
  - path: data/eval.jsonl
    ds_type: json
    # You need to specify a split. For "json" datasets the default split is called "train".
    split: train
    type: sharegpt
    data_files:
      - data/eval.jsonl

sequence_len: 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 2
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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

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: <|finetune_right_pad_id|>
  eos_token: <|eot_id|>

```

</details><br>

# model-out

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: 0.5353

## 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: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9615        | 0.0696 | 1    | 0.9211          |
| 0.8231        | 0.2783 | 4    | 0.9140          |
| 0.779         | 0.5565 | 8    | 0.8034          |
| 0.545         | 0.8348 | 12   | 0.6488          |
| 0.3712        | 1.1043 | 16   | 0.5825          |
| 0.336         | 1.3826 | 20   | 0.5492          |
| 0.3231        | 1.6609 | 24   | 0.5379          |
| 0.2729        | 1.9391 | 28   | 0.5353          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1