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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:
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.
| 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 | 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 |
On Pidgin English — every competing model scored 0.00. Africlaude scored 1.00.
Peak score of 0.918 achieved at Cycle 9 NB3. Final released model scored 0.791 on AfriclaudeBench v1.0 against industry models.
| 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 |
| 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:
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport 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))python
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)Pidgin English
text
User: Wetin be Paystack webhook?Africlaude: Paystack webhook na like callback URL wey Paystack go usesend notification to your server when something happen — like whencustomer pay, when payment fail, or when refund process. You go needverify the signature for header to make sure na real Paystack send am,not fake request.Nigerian Fintech
text
User: Explain CBN KYC tiers for a Nigerian fintechAfriclaude: CBN KYC tiers in Nigeria:Tier 1 (Basic): Maximum balance N300,000, daily transaction limitN50,000. Requires BVN or NIN only.Tier 2: Maximum balance N500,000, daily limit N200,000.Requires BVN + one government-issued ID...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.
bibtex
@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.}}Apache 2.0 — free to use, modify, and build upon commercially. See LICENSE.
Built by Emmanuel Ariyo | Axiveri
AfriclaudeBench v1.0 | Axiveri | All models tested live from HuggingFace
| 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 |
| Emmanuel Ariyo |
| Organization | Axiveri |