Instructions to use Axiveri/Africlaude-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Axiveri/Africlaude-7B with Transformers:

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

pipe = pipeline("text-generation", model="Axiveri/Africlaude-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Axiveri/Africlaude-7B")
model = AutoModelForMultimodalLM.from_pretrained("Axiveri/Africlaude-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Axiveri/Africlaude-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Axiveri/Africlaude-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Axiveri/Africlaude-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Axiveri/Africlaude-7B

SGLang

How to use Axiveri/Africlaude-7B 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 "Axiveri/Africlaude-7B" \
    --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": "Axiveri/Africlaude-7B",
		"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 "Axiveri/Africlaude-7B" \
        --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": "Axiveri/Africlaude-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Axiveri/Africlaude-7B with Docker Model Runner:
```
docker model run hf.co/Axiveri/Africlaude-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Africlaude-7B

Africa's own language model.
Built for African developers, Nigerian fintech engineers, and local innovation.

First model in the Africlaude series by Axiveri

Overview

Africlaude-7B is a 7-billion parameter language model built specifically for African developers, Nigerian fintech companies, and the African tech ecosystem. This is not a consumer chatbot. It is AI infrastructure — a foundation that African companies and developers build upon.

Most AI models are trained on Western-default data. They confuse kobo with cents. They have never seen a USSD flow, a Paystack webhook, or a CBN regulatory directive. Africlaude-7B was built to fix that.

What it understands natively:

Nigerian English and Pidgin English
USSD session management and AfricasTalking workflows
Paystack, Flutterwave, and African payment API integrations
CBN KYC tiers, licensing requirements, and compliance contexts
Nigerian fintech fraud patterns and security awareness
African developer coding patterns and tooling

Benchmark Results

Africlaude-7B was evaluated on AfriclaudeBench v1.0 — an open benchmark that loads and tests all models live from HuggingFace across 9 African domains. No estimates. No simulation. Real inference, real scores.

Overall Score Ranking

Model	Overall Score
🥇 Africlaude-7B (Axiveri)	0.791
LLaMA-3.1-8B (Meta)	0.566
Qwen2.5-Coder-7B (Base)	0.555
Mistral-7B-v0.3 (Mistral AI)	0.533

Category Breakdown

Category	Africlaude-7B	LLaMA	Mistral
Fintech	0.86	0.36	0.43
Security	0.75	0.75	0.75
Pidgin English	1.00	1.00	0.00
Identity	1.00	0.00	0.00
Fraud Detection	1.00	1.00	1.00
SQL/DB	1.00	1.00	1.00
USSD Engineering	0.56	0.56	0.78
Reasoning	0.50	0.50	0.50
CBN Compliance	0.33	0.00	0.33

On Pidgin English — every competing model scored 0.00. Africlaude scored 1.00.

Full Heatmap

Domain Mastery Radar

Fine-Tuning Impact vs Base Model

Score Progression Over 10 Training Cycles

Peak score of 0.918 achieved at Cycle 9 NB3. Final released model scored 0.791 on AfriclaudeBench v1.0 against industry models.

Model Details

Property	Value
Model Name	Africlaude-7B
Model ID	`Axiveri/Africlaude-7B`
Series	Africlaude (first model in the series)
Parameters	7B
Format	Merged float16 — fully standalone, no external dependencies
License	Apache 2.0
Languages	English, Nigerian Pidgin, Nigerian English
Developer	Emmanuel Ariyo
Organization	Axiveri

Training Details

Property	Value
Hardware	NVIDIA Tesla T4 x2
VRAM	~15.6 GB per GPU
Total GPU Hours	~100 hours
Training Duration	2 months
Training Cycles	10 cycles
Fine-tuning Method	QLoRA (4-bit quantized LoRA)
LoRA Rank	r=16, alpha=32
Framework	Unsloth + TRL + HuggingFace Transformers

Training Data composition:

Nigerian fintech workflows (Paystack, Flutterwave, CBN compliance)
Pidgin English instruction pairs
USSD session engineering
African fraud detection and security awareness
General reasoning (OpenR1-Math, GSM8K, MetaMath, NuminaMath)
Code generation (CodeFeedback, OpenHermes)
Financial instruction following (Finance-Alpaca)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Axiveri/Africlaude-7B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "How do I verify a Paystack webhook in Python?"}
]

text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With Unsloth (Faster Inference)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="Axiveri/Africlaude-7B",
    max_seq_length=2048,
    dtype=torch.float16,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

Example Outputs

Pidgin English

User: Wetin be Paystack webhook?

Africlaude: Paystack webhook na like callback URL wey Paystack go use
send notification to your server when something happen — like when
customer pay, when payment fail, or when refund process. You go need
verify the signature for header to make sure na real Paystack send am,
not fake request.

Nigerian Fintech

User: Explain CBN KYC tiers for a Nigerian fintech

Africlaude: CBN KYC tiers in Nigeria:

Tier 1 (Basic): Maximum balance N300,000, daily transaction limit
N50,000. Requires BVN or NIN only.

Tier 2: Maximum balance N500,000, daily limit N200,000.
Requires BVN + one government-issued ID...

Known Limitations

CBN Compliance (0.33) and Reasoning (0.50) are below target — addressed in the next training cycle
AfriclaudeBench v1.0 has known scoring limitations; v2.0 is in development with harder, more African-specific questions
Primarily optimised for Nigerian English and Pidgin; broader African language support (Yoruba, Hausa, Igbo) is on the roadmap

What's Next

Africlaude-7B is the first model in the Africlaude series — the learning and educational foundation for African developers and fintech teams. More powerful models with broader African language support, multimodal capabilities, and production-grade fintech engineering are in active development.

Follow @Africlaude and @Axiveri for updates.

Citation

@misc{africlaude7b2026,
  title={Africlaude-7B: African-Standard Language Model for Fintech and Developer Contexts},
  author={Emmanuel Ariyo},
  organization={Axiveri},
  year={2026},
  url={https://huggingface.co/Axiveri/Africlaude-7B},
  note={Fine-tuned language model. Evaluated on AfriclaudeBench v1.0
        across 9 African domains. 10 training cycles on Tesla T4 x2.}
}

License

Apache 2.0 — free to use, modify, and build upon commercially. See LICENSE.

Built by Emmanuel Ariyo | Axiveri

@Africlaude · @Axiveri

AfriclaudeBench v1.0 | Axiveri | All models tested live from HuggingFace

Downloads last month: 340

Safetensors

Model size

8B params

Tensor type

F16

Model tree for Axiveri/Africlaude-7B

Quantizations

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