metadata
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: peft
pipeline_tag: text-generation
license: apache-2.0
language:
- en
- pcm
tags:
- lora
- peft
- nigeria
- nigerian-pidgin
- business-assistant
GaiaLab Naija Adapter v0.1
GaiaLab Naija Adapter v0.1 is an experimental LoRA adapter trained on
Qwen/Qwen2.5-0.5B-Instruct.
It explores Nigerian small-business communication, business writing, Nigerian English, and Nigerian Pidgin.
Intended use
The adapter is designed for research and experimentation involving:
- Nigerian small-business customer communication
- Professional business-writing assistance
- English-to-Nigerian-Pidgin translation
- Nigerian-Pidgin-to-English translation
- Explanation of common business terminology
Training data
- 100 curated examples
- 80 training records
- 20 validation records
- 5 task categories
The dataset includes:
- Customer-service responses
- Nigerian business terminology
- English-to-Nigerian-Pidgin translation
- Nigerian-Pidgin-to-English translation
- Business writing
Training configuration
- Base model:
Qwen/Qwen2.5-0.5B-Instruct - Fine-tuning method: LoRA
- Epochs: 3
- Training records: 80
- Validation records: 20
Training results
- Final training loss:
2.342 - Final evaluation loss:
2.106
These results confirm that the adapter-training pipeline completed successfully. They do not prove that the adapter is more accurate than the base model across all tasks.
Preliminary evaluation
Initial testing showed mixed results:
- Business-writing responses were generally clear and professional.
- Customer-service responses were polite but sometimes omitted requested details.
- Nigerian Pidgin translation quality was inconsistent.
- Some terminology responses contained factual errors.
- The model occasionally added information that was not present in the prompt.
This release should therefore be treated as a research prototype.
Limitations
The model may:
- Produce inaccurate information
- Change or omit details during translation
- Generate unnatural Nigerian Pidgin
- Misunderstand cultural or business context
- Add unsupported facts or explanations
- Hallucinate business, legal, tax, financial, or regulatory guidance
Human review is required before real-world use.
Loading the adapter
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model_id = "Qwen/Qwen2.5-0.5B-Instruct"
adapter_id = "mgbam/gaialab-naija-adapter-v0.1"
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id,
dtype=torch.float16,
device_map="auto"
)
model = PeftModel.from_pretrained(
base_model,
adapter_id
)
model.eval()
Load the tokenizer from the base model because it contains the correct
Qwen chat template.
Model repository
Hugging Face:
mgbam/gaialab-naija-adapter-v0.1
Source code
GitHub:
oluwafemidiakhoa/gaialab-naija-assistant
Disclaimer
This is an experimental research release. Generated responses should be
reviewed before use in business, legal, financial, tax, medical, or
regulatory contexts.