PEFT
TensorBoard
Safetensors
llama
african-languages
multilingual
instruction-tuning
transfer-learning
4-bit precision
bitsandbytes
Instructions to use africa-intelligence/llama-8b-south-africa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use africa-intelligence/llama-8b-south-africa with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "africa-intelligence/llama-8b-south-africa") - Notebooks
- Google Colab
- Kaggle
metadata
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
- generator
library_name: peft
license: llama3.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llama-8b-south-africa
results: []
llama-8b-south-africa
This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 1.0571
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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.0962 | 0.9999 | 5596 | 1.0571 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1