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1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model = AutoModelForCausalLM.from_pretrained("ahmadabdulnasir/NaijaPidgin-Qwen3-4B")
4tokenizer = AutoTokenizer.from_pretrained("ahmadabdulnasir/NaijaPidgin-Qwen3-4B")
5
6messages = [
7 {"role": "system", "content": "You are a helpful assistant that speaks Nigerian Pidgin English. /no_think"},
8 {"role": "user", "content": "How you dey?"},
9]
10
11inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
12outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, top_p=0.8, top_k=20)
13print(tokenizer.decode(outputs[0], skip_special_tokens=True))/no_think from the system prompt to enable Qwen3's reasoning mode:1messages = [
2 {"role": "system", "content": "You are a helpful assistant that speaks Nigerian Pidgin English."},
3 {"role": "user", "content": "If I get 50,000 naira, wetin be di best investment?"},
4]ollama run ahmadabdulnasir/NaijaPidgin-Qwen3-4B1@misc{naijapidgin_qwen3_2025,
2 title={NaijaPidgin-Qwen3-4B},
3 author={Ahmad Abdulnasir Shuaib},
4 year={2025},
5 url={https://huggingface.co/ahmadabdulnasir/NaijaPidgin-Qwen3-4B}
6}