| --- |
| language: |
| - en |
| - ig |
| - ha |
| - yo |
| license: apache-2.0 |
| base_model: McGill-NLP/AfriqueQwen-8B |
| tags: |
| - tool-calling |
| - function-calling |
| - igbo |
| - hausa |
| - yoruba |
| - nigeria |
| - voice-assistant |
| --- |
| |
| # MadamLLM-Tools |
|
|
| **Madam** is a Nigerian multilingual voice assistant fine-tuned for native tool calling across Igbo, Hausa, Yoruba, and English. |
|
|
| This model (`madamLLM-tools`) extends [`babs/madamLLM-dpo`](https://huggingface.co/babs/madamLLM-dpo) with tool calling capabilities using Qwen3's native Hermes-style `<tool_call>` format. |
|
|
| ## Model Details |
|
|
| - **Base model:** [McGill-NLP/AfriqueQwen-8B](https://huggingface.co/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. |
|
|
