---
base_model: barc0/llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4
datasets:
- barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems
- barc0/transduction_augmented_test_timearc_all_evaluation_new_seperate
- barc0/transduction_rearc_dataset_400k
library_name: peft
license: llama3.2
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
model-index:
- name: barc-llama3.2-1b-instruct-lora64-testtime-finetuning
  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. -->

# barc-llama3.2-1b-instruct-lora64-testtime-finetuning

This model is a fine-tuned version of [barc0/llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4](https://huggingface.co/barc0/llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4) on the barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems, the barc0/transduction_augmented_test_timearc_all_evaluation_new_seperate and the barc0/transduction_rearc_dataset_400k datasets.
It achieves the following results on the evaluation set:
- Loss: 0.0591

## 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: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.074         | 1.0   | 1355 | 0.0688          |
| 0.045         | 2.0   | 2710 | 0.0575          |
| 0.0207        | 3.0   | 4065 | 0.0591          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1