Instructions to use smallsuper/Meta-Llama-3-8B-4bit-32rank with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smallsuper/Meta-Llama-3-8B-4bit-32rank with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="smallsuper/Meta-Llama-3-8B-4bit-32rank")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("smallsuper/Meta-Llama-3-8B-4bit-32rank") model = AutoModel.from_pretrained("smallsuper/Meta-Llama-3-8B-4bit-32rank") - Notebooks
- Google Colab
- Kaggle
Overview
This is a bare model without any output layer or classification head. It has been quantized to be used for feature extraction tasks.
Usage
This model is intended to be used as a base for training on downstream tasks. In order to use it for predictions and inference, it should be fine-tuned on a specific task with an appropriate output layer or classification head added.
Quantization
The model has been quantized using the following parameters:
Lora alpha: 16
Lora rank: 32
Lora target modules: all-linear
bits: 4
LoftQ iterations: 5
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