---
base_model: meta-llama/Llama-3.1-8B-Instruct
datasets:
- codezakh/EFAGen-Llama-3.1-8B-Instruct-Training-Data
library_name: transformers
license: other
pipeline_tag: text-generation
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: llama_factory_output_dir
  results: []
---

[📃 Paper](arxiv.org/abs/2504.09763)

Project Page: https://zaidkhan.me/EFAGen

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) trained to generate Executable Functional Abstractions (EFAs) for math problems.
The training data for this model can be found [here](https://huggingface.co/datasets/codezakh/EFAGen-Llama-3.1-8B-Instruct-Training-Data).
The model was trained using Llama-Factory and the data is already in Alpaca instruction-tuning format.
The "Instruction" field contains a prompt with instructions defining the EFA protocol and a set of static in-context examples (they're the same for all rows).
The "Response" field contains the code of the EFA. 


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1