---
library_name: peft
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
datasets:
- teknium/GPT4-LLM-Cleaned
model-index:
- name: llama-fr-lora
  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: `0.6.0`
```yaml
adapter: lora
base_model: NousResearch/Llama-3.2-1B
bf16: auto
dataset_prepared_path: last_run_prepared
datasets:
- path: teknium/GPT4-LLM-Cleaned
  type: alpaca
eval_sample_packing: true
evals_per_epoch: 4
flash_attention: true
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: pandyamarut/llama-fr-lora
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_r: 16
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
loss_watchdog_patience: 3
loss_watchdog_threshold: 5
lr_scheduler: cosine
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_8bit
output_dir: /runpod-volume/fine-tuning/test-run
pad_to_sequence_len: true
run_name: test-run
runpod_job_id: dd327f42-5f67-4830-b512-4561fa9a3d45-u1
sample_packing: true
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
  pad_token: <|end_of_text|>
strict: false
tf32: false
train_on_inputs: false
val_set_size: 0.1
wandb_entity: axo-test
wandb_name: test-run-1
wandb_project: test-run-1
warmup_steps: 10
weight_decay: 0

```

</details><br>

# llama-fr-lora

This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the teknium/GPT4-LLM-Cleaned dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1018

## 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: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_8BIT 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: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4537        | 0.0009 | 1    | 1.3971          |
| 1.1978        | 0.2503 | 271  | 1.1561          |
| 1.1637        | 0.5007 | 542  | 1.1131          |
| 1.1894        | 0.7510 | 813  | 1.1018          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0