README.md

---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
- pytorch
base_model: microsoft/phi-2
model-index:
- name: phi-2-basic-maths
  results:
  # AI2 Reasoning Challenge (25-Shot)
  - task: 
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
       - type: acc_norm
         name: normalized accuracy
         value: 62.03071672354948
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta

  # HellaSwag (10-shot)
  - task: 
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
       - type: acc_norm
         name: normalized accuracy
         value: 84.35570603465445
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta

datasets:
- gsm8k
source:
  name: Open LLM Leaderboard
  url: >-
    https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Menouar/phi-2-basic-maths
language:
- en
pipeline_tag: text-generation
---

# phi-2-basic-maths

This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an [GSM8K dataset](https://huggingface.co/datasets/gsm8k).

## 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:

```python
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](https://colab.research.google.com/drive/1mvfoEqc0mwuf8FqrABWt06qwAsU2QrvK). 

### 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](https://huggingface.co/Menouar/phi-2-basic-maths/tensorboard).

## Evaluation procedure

The complete Evaluation procedure can be found on my [Notebook](https://colab.research.google.com/drive/1xsdxOm-CgZmLAPFgp8iU9lLFEIIHGiUK).

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