Instructions to use Striker-7/elephi3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Striker-7/elephi3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct") model = PeftModel.from_pretrained(base_model, "Striker-7/elephi3") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Striker-7/elephi3: direct link, hf CLI and curl.
- Browser
- Download file 1.12 kB
-
https://huggingface.co/Striker-7/elephi3/resolve/main/README.md
- Command line
-
hf download hf://Striker-7/elephi3/README.md
-
curl -L -o README.md https://huggingface.co/Striker-7/elephi3/resolve/main/README.md
1.12 kB
metadata
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: elephi3
results: []
elephi3
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-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: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.42.4
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1