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DannyAI/
phi4_african_history_lora_ds2

Name: phi4_african_history_lora_ds2
Availability: InStock

DannyAI/African-History-QA-Dataset

microsoft/Phi-4-mini-instruct

mit

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Model Card for Model ID

This is a LoRA fine-tuned version of microsoft/Phi-4-mini-instruct for African History using the DannyAI/African-History-QA-Dataset dataset. It achieves a loss value of 1.5099 on the validation set

Model Details

Model Description

Developed by: Daniel Ihenacho
Funded by: Daniel Ihenacho
Shared by: Daniel Ihenacho
Model type: Text Generation
Language(s) (NLP): English
License: mit
Finetuned from model: microsoft/Phi-4-mini-instruct

Uses

This can be used for QA datasets about African History

Out-of-Scope Use

Can be used beyond African History but should not.

How to Get Started with the Model

python

from transformers import pipeline
from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM)
from peft import PeftModel


model_id = "microsoft/Phi-4-mini-instruct"

tokeniser = AutoTokenizer.from_pretrained(model_id)

# load base model
model  = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map = "auto",
    torch_dtype = torch.bfloat16,
    trust_remote_code = False
)

# Load the fine-tuned LoRA model
lora_id = "DannyAI/phi4_african_history_lora_ds2"
lora_model = PeftModel.from_pretrained(
    model,lora_id
)

generator = pipeline(
    "text-generation",
    model=lora_model,
    tokenizer=tokeniser,
)
question = "What is the significance of African feminist scholarly activism in contemporary resistance movements?"
def generate_answer(question)->str:
    """Generates an answer for the given question using the fine-tuned LoRA model.
    """
    messages = [
        {"role": "system", "content": "You are a helpful AI assistant specialised in African history which gives concise answers to questions asked."},
        {"role": "user", "content": question}
    ]
    
    output = generator(
        messages, 
        max_new_tokens=2048, 
        temperature=0.1, 
        do_sample=False,
        return_full_text=False
    )
    return output[0]['generated_text'].strip()

# Example output
African feminist scholarly activism is significant in contemporary resistance movements as it provides a critical framework for understanding and addressing the specific challenges faced by African women in the context of global capitalism, neocolonialism, and patriarchal structures.

Training Details

Training Data

Training Loss	Epoch	Step	Validation Loss
1.6515	0.3784	100	1.6736
1.5844	0.7569	200	1.6175
1.6068	1.1325	300	1.5855
1.6075	1.5109	400	1.5679
1.5188	1.8893	500	1.5525
1.4248	2.2649	600	1.5423
1.5465	2.6433	700	1.5363
1.454	3.0189	800	1.5331
1.5759	3.3974	900	1.5275
1.4626	3.7758	1000	1.5268
1.4861	4.1514	1100	1.5230
1.4863	4.5298	1200	1.5232
1.4312	4.9082	1300	1.5185
1.5311	5.2838	1400	1.5193
1.5135	5.6623	1500	1.5179
1.4092	6.0378	1600	1.5144
1.5621	6.4163	1700	1.5145
1.485	6.7947	1800	1.5147
1.4301	7.1703	1900	1.5109
1.5346	7.5487	2000	1.5156
1.4597	7.9272	2100	1.5124
1.4548	8.3027	2200	1.5118
1.4485	8.6812	2300	1.5108
1.4466	9.0568	2400	1.5116
1.4672	9.4352	2500	1.5132
1.4881	9.8136	2600	1.5099

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Deep Speed Configuration

json

{
    "fp16": { "enabled": false },
    "bf16": { "enabled": true },
    "zero_optimization": {
        "stage": 2,
        "offload_optimizer": { 
            "device": "cpu", 
            "pin_memory": true 
        },
        "overlap_comm": true,
        "contiguous_gradients": true,
        "reduce_bucket_size": "auto"
    },
    "gradient_accumulation_steps": "auto",
    "gradient_clipping": "auto",
    "train_batch_size": "auto",
    "train_micro_batch_size_per_gpu": "auto"
}

Lora Configuration

r: 8
lora_alpha: 16
target_modules: ["q_proj", "v_proj", "k_proj", "o_proj"]
lora_dropout: 0.05 # dataset is small, hence a low dropout value
bias: "none"
task_type: "CAUSAL_LM"

Evaluation

Metrics

Models	Bert Score	TinyMMLU	TinyTrufulQA
Base model	0.88868	0.6837	0.49745
Fine tuned Model	0.90726	0.67788	0.43822

Compute Infrastructure

Runpod.

Hardware

Runpod A40 GPU instance

Framework versions

PEFT 0.18.1
Transformers 4.57.6
Pytorch 2.4.1+cu124
Datasets 4.5.0
Tokenizers 0.22.2

Citation

If you use this dataset, please cite:

@Model{
Ihenacho2026phi4_african_history_lora_ds2,
  author    = {Daniel Ihenacho},
  title     = {phi4_african_history_lora_ds2},
  year      = {2026},
  publisher = {Hugging Face Models},
  url       = {https://huggingface.co/DannyAI/phi4_african_history_lora_ds2},
  urldate   = {2026-01-27},
}

Model Card Authors

Daniel Ihenacho

DannyAI/phi4_african_history_lora_ds2