Base model: McGill-NLP/AfriqueQwen-8B (Qwen3-8B continued pretrained on 27.5B tokens across 20 African languages)
Training stages: SFT → SimPO DPO → Tool Calling SFT (this model)
Method: LoRA SFT (rank 64, alpha 128, RSLoRA, all linear layers + lm_head)
Framework: LLaMA-Factory
Hardware: 2× NVIDIA A100 SXM 80GB

Tool Calling Evaluation

Metric	Score
Tool calling accuracy	20/20 — 100%
Irrelevance detection	10/10 — 100%
Args in English	19/20 — 95%
Weighted overall	99.0%

All four languages scored 100% on tool accuracy: English ✅ Igbo ✅ Hausa ✅ Yoruba ✅

Supported Tools

Madam knows 8 Nigeria-specific tools out of the box:

Tool	Description
`get_weather`	Current weather for Nigerian cities
`get_exchange_rate`	NGN exchange rates
`get_news`	Nigerian news headlines
`get_market_prices`	Commodity prices (rice, garri, fuel, etc.)
`search_health_info`	Basic health and first-aid guidance
`get_prayer_times`	Islamic prayer times
`send_message`	Send SMS/WhatsApp messages
`set_reminder`	Set time-based reminders

Tool Calling Format

Uses Qwen3's native Hermes-style format. Pass tools via apply_chat_template:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("babs/madamLLM-tools", torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("babs/madamLLM-tools")

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather conditions for a city in Nigeria",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "City name, e.g. Lagos, Kano, Abuja"}
                },
                "required": ["city"]
            }
        }
    }
]

messages = [
    {"role": "system", "content": "You are Madam, a Nigerian voice assistant."},
    {"role": "user", "content": "Yaya yanayi a Kano yau?"}  # Hausa: What's the weather in Kano today?
]

text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=200, temperature=0.3, do_sample=True, eos_token_id=[151645, 151643])
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))
# → <tool_call>{"name": "get_weather", "arguments": {"city": "Kano"}}</tool_call>

vLLM Serving

bash

vllm serve babs/madamLLM-tools \
    --tool-call-parser hermes \
    --enable-auto-tool-choice \
    --dtype bfloat16 \
    --max-model-len 4096

Training Data

Source	Examples	Purpose
Synthetic (Groq gpt-oss-120b)	11,924	Nigeria-specific tool calls, 4 languages
APIGen-MT-5k	1,009	Multi-turn tool chains
xLAM-60k sample	2,500	Diverse tool schemas (1,240 unique tools)
Hermes FC v1	1,100	Format alignment
SFT replay	4,000	Anti-forgetting (Igbo/Hausa/Yoruba/English)
Total	20,533

50% of tool-calling examples use function masking (Hammer technique) for generalization to unseen tools.

Soul

Madam embodies the nine fruits of the Spirit (Galatians 5:22-23): love, joy, peace, patience, kindness, goodness, faithfulness, gentleness, and self-control.