---
library_name: peft
license: other
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- base_model:adapter:meta-llama/Llama-3.2-1B-Instruct
- llama-factory
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: finetune_output
  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. -->

# finetune_output

This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the gnaf-2022-structured-training-1000000-v0-instruct-train dataset.

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- PEFT 0.17.1
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
### Full training details

model_version='v0.2'
aest_now='20251019-170453'
target_model_name='dylanhogg/gnaf-structured-address-v0.2-712c28b-20251019-170453'
train_args_hash='712c28b'
train_args=
{
  "stage": "sft",
  "do_train": true,
  "model_name_or_path": "meta-llama/Llama-3.2-1B-Instruct",
  "dataset": "gnaf-2022-structured-training-1000000-v0-instruct-train",
  "eval_dataset": "gnaf-2022-structured-training-1000000-v0-instruct-test",
  "template": "llama3",
  "finetuning_type": "lora",
  "lora_target": "all",
  "output_dir": "finetune_output",
  "plot_loss": true,
  "per_device_train_batch_size": 2,
  "gradient_accumulation_steps": 4,
  "lr_scheduler_type": "cosine",
  "logging_steps": 5,
  "warmup_ratio": 0.1,
  "save_steps": 1000,
  "learning_rate": 5e-05,
  "num_train_epochs": 2.0,
  "max_samples": 10000,
  "max_grad_norm": 1.0,
  "loraplus_lr_ratio": 16.0,
  "fp16": true,
  "report_to": "none"
}