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--- |
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license: mit |
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library_name: peft |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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base_model: microsoft/phi-2 |
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model-index: |
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- name: phi-2-basic-maths |
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results: [] |
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datasets: |
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- gsm8k |
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metrics: |
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- type: accuracy |
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name: Accuracy |
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value: 36.16% |
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--- |
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# phi-2-basic-maths |
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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). |
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## Model Description |
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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. |
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To test it, you can use the following code: |
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```python |
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import torch |
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from peft import AutoPeftModelForCausalLM |
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from transformers import AutoTokenizer, pipeline |
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# Specify the model ID |
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peft_model_id = "Menouar/phi-2-basic-maths" |
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# Load Model with PEFT adapter |
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model = AutoPeftModelForCausalLM.from_pretrained( |
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peft_model_id, |
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device_map="auto", |
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torch_dtype=torch.float16 |
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) |
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tokenizer = AutoTokenizer.from_pretrained(peft_model_id) |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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``` |
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## Training procedure |
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The complete training procedure can be found on my [Notebook](https://colab.research.google.com/drive/1mvfoEqc0mwuf8FqrABWt06qwAsU2QrvK). |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 42 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 84 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: constant |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 30 |
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### Training results |
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The training results can be found on [Tensoboard](https://huggingface.co/Menouar/phi-2-basic-maths/tensorboard). |
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## Evaluation procedure |
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The complete Evaluation procedure can be found on my [Notebook](https://colab.research.google.com/drive/1xsdxOm-CgZmLAPFgp8iU9lLFEIIHGiUK). |
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Accuracy: 36.16% |
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Unclear answers: 7.81% |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
