README.md

metadata

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:
      - 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
      - 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 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