Instructions to use DannyAI/phi4_african_history_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DannyAI/phi4_african_history_lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DannyAI/phi4_african_history_lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DannyAI/phi4_african_history_lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DannyAI/phi4_african_history_lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DannyAI/phi4_african_history_lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DannyAI/phi4_african_history_lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DannyAI/phi4_african_history_lora

SGLang

How to use DannyAI/phi4_african_history_lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DannyAI/phi4_african_history_lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DannyAI/phi4_african_history_lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DannyAI/phi4_african_history_lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DannyAI/phi4_african_history_lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DannyAI/phi4_african_history_lora with Docker Model Runner:
```
docker model run hf.co/DannyAI/phi4_african_history_lora
```

phi4_african_history_lora / README.md

DannyAI

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metadata

library_name: transformers
tags:
  - history
  - conversational
  - PEFT
license: mit
datasets:
  - DannyAI/African-History-QA-Dataset
language:
  - en
metrics:
  - bertscore
base_model:
  - microsoft/Phi-4-mini-instruct
pipeline_tag: text-generation

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 1.488960 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

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"
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

Step	Training Loss	Validation Loss
100	1.643300	1.649192
200	1.546300	1.576022
300	1.580200	1.552545
400	1.575900	1.538777
500	1.499500	1.529112
600	1.400600	1.516559
700	1.524000	1.513925
800	1.437100	1.507401
900	1.547300	1.504273
1000	1.441300	1.502129
1100	1.452500	1.499649
1200	1.466400	1.495797
1300	1.407500	1.494715
1400	1.511400	1.493275
1500	1.489600	1.495470
1600	1.384400	1.492817
1700	1.534900	1.490099
1800	1.469300	1.490490
1900	1.407500	1.488490
2000	1.512300	1.487388
2100	1.438900	1.490232
2200	1.434700	1.490498
2300	1.421200	1.489342
2400	1.418200	1.487220
2500	1.441200	1.487831
2600	1.453900	1.488960

Training Hyperparameters

per_device_train_batch_size=2
gradient_accumulation_steps = 4
learning_rate=2e-5
num_train_epochs=10
bf16=True
eval_strategy="steps"
eval_steps=100,
save_strategy="steps"
save_steps=200
logging_steps=10

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	0.67751	0.43555

Compute Infrastructure

Runpod.

Hardware

Runpod A40 GPU instance

Citation

If you use this dataset, please cite:

@Model{
Ihenacho2026phi4_african_history_lora,
  author    = {Daniel Ihenacho},
  title     = {phi4_african_history_lora},
  year      = {2026},
  publisher = {Hugging Face Models},
  url       = {https://huggingface.co/DannyAI/phi4_african_history_lora},
  urldate   = {2026-01-27},
}

Model Card Authors

Daniel Ihenacho

DannyAI
/

phi4_african_history_lora