Instructions to use migleolop/FTmodel7-24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use migleolop/FTmodel7-24 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "migleolop/FTmodel7-24") - Notebooks
- Google Colab
- Kaggle
metadata
license: llama2
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-chat-hf
model-index:
- name: FTmodel7-24
results: []
FTmodel7-24
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on the None 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.11.1.dev0
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1