saheedniyi/Llama3-8b-Naija_v1

Excited to announce the release of Llama3-8b-Naija_v1 a finetuned version of Meta-Llama-3-8B trained on a Question - Answer dataset from Nairaland. The model was built in an attempt to "Nigerialize" Llama-3, giving it a Nigerian - like behavior.

Model Details

Model Description

• Developed by: Saheedniyi
• Language(s) (NLP): English, Pidgin English
• License: META LLAMA 3 COMMUNITY LICENSE AGREEMENT
• Finetuned from : meta-llama/Meta-Llama-3-8B

Model Sources

• Repository
• Demo: Colab Notebook

How to Get Started with the Model

Use the code below to get started with the model.

#necessary installations
!pip install bitsandbytes peft accelerate

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("saheedniyi/Llama3-8b-Naija_v1")
model = AutoModelForCausalLM.from_pretrained("saheedniyi/Llama3-8b-Naija_v1")

input_text = "What are the top places for tourism in Nigeria?"
formatted_prompt = f"### BEGIN CONVERSATION ###\n\n## User: ##\n{input_text}\n\n## Assistant: ##\n"
inputs = tokenizer(formatted_prompt, return_tensors="pt")
outputs = model.generate(**inputs.to("cuda"), max_new_tokens=512,pad_token_id=tokenizer.pad_token_id,do_sample=True,temperature=0.6,top_p=0.9,)
response=tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

when using the model it is important to use the chat template that the model was trained on.

prompt = "INPUT YOUR PROMPT HERE"
formatted_prompt=f"### BEGIN CONVERSATION ###\n\n## User: ##\n{prompt}\n\n## Assistant: ##\n"

The model has a little tokenization issue and it's necessary to wtrite a function to clean the output to make it cleaner and more presentable.

def split_response(text):
  return text.split("### END CONVERSATION")[0]

cleaned_response=split_response(response)
print(cleaned_response)

This issue shold be resolved in the next version of the model.