Instructions to use w11wo/Llama-3.2-1B-FourSquare-NYC-POI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use w11wo/Llama-3.2-1B-FourSquare-NYC-POI with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B") model = PeftModel.from_pretrained(base_model, "w11wo/Llama-3.2-1B-FourSquare-NYC-POI") - Notebooks
- Google Colab
- Kaggle
metadata
base_model: meta-llama/Llama-3.2-1B
library_name: peft
tags:
- generated_from_trainer
model-index:
- name: Llama-3.2-1B-FourSquare-NYC-POI
results: []
Llama-3.2-1B-FourSquare-NYC-POI
This model is a fine-tuned version of meta-llama/Llama-3.2-1B 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 3
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1