Instructions to use yizhujiao/llama2-7b-sft-math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yizhujiao/llama2-7b-sft-math with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct") model = PeftModel.from_pretrained(base_model, "yizhujiao/llama2-7b-sft-math") - Notebooks
- Google Colab
- Kaggle
llama2-7b-sft-math
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on an unknown 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: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.40.1
- Pytorch 2.5.0.dev20240624+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for yizhujiao/llama2-7b-sft-math
Base model
meta-llama/Meta-Llama-3-8B-Instruct