metadata

license: mit
library_name: peft
tags:
  - trl
  - sft
  - generated_from_trainer
base_model: microsoft/phi-2
model-index:
  - name: phi-2-basic-maths
    results: []
datasets:
  - gsm8k
metrics:
  - type: accuracy
    name: Accuracy
    value: 36.16%

phi-2-basic-maths

This model is a fine-tuned version of microsoft/phi-2 on an GSM8K dataset.

Model Description

The objective of this model is to evaluate Phi-2's ability to provide correct solutions to reasoning problems after fine-tuning. This model was trained using techniques such as TRL, LoRA quantization, and Flash Attention.

To test it, you can use the following code:

import torch
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer, pipeline

# Specify the model ID
peft_model_id = "Menouar/phi-2-basic-maths"

# Load Model with PEFT adapter
model = AutoPeftModelForCausalLM.from_pretrained(
  peft_model_id,
  device_map="auto",
  torch_dtype=torch.float16
)

tokenizer = AutoTokenizer.from_pretrained(peft_model_id)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

Training procedure

The complete training procedure can be found on my Notebook.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 42
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 84
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.03
num_epochs: 30

Training results

The training results can be found on Tensoboard.

Evaluation procedure

The complete Evaluation procedure can be found on my Notebook.

Accuracy: 36.16% Unclear answers: 7.81%

Framework versions

PEFT 0.8.2
Transformers 4.38.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1