Text Generation
PEFT
TensorBoard
Safetensors
PyTorch
English
trl
sft
Generated from Trainer
Eval Results (legacy)
Instructions to use Menouar/phi-2-basic-maths with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Menouar/phi-2-basic-maths with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2") model = PeftModel.from_pretrained(base_model, "Menouar/phi-2-basic-maths") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Menouar/phi-2-basic-maths: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/Menouar/phi-2-basic-maths/resolve/dcdc93464a7ceb6767e48639ef73751ae9c8b355/README.md
- Command line
-
hf download hf://Menouar/phi-2-basic-maths@dcdc93464a7ceb6767e48639ef73751ae9c8b355/README.md
-
curl -L -o README.md https://huggingface.co/Menouar/phi-2-basic-maths/resolve/dcdc93464a7ceb6767e48639ef73751ae9c8b355/README.md
1.21 kB
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
phi-2-basic-maths
This model is a fine-tuned version of microsoft/phi-2 on GSMK8 dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
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: 10
Training results
Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1