from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "bigatuna/Qwen3-1.7B-Sushi-Coder",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("bigatuna/Qwen3-1.7B-Sushi-Coder")

messages = [
    {"role": "user", "content": "Write a Python function to solve the two-sum problem."}
]

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, temperature=0.6, top_p=0.95, top_k=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Sampling Parameters

For best results with Qwen3 models:

Temperature: 0.6-0.7
Top-p: 0.95
Top-k: 20
Do not use greedy decoding (temp=0 causes repetitions)

License

Apache 2.0