from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "Ljy2004/mapfinben-qwen3-merged-unified-v2"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path, trust_remote_code=True, torch_dtype="auto", device_map="auto"
)

messages = [{"role": "user", "content": "YOUR_PROMPT"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)

Important: Use Qwen3 chat template with enable_thinking=False to match training.

Citation

Base model: <a href="https://huggingface.co/Qwen/Qwen3-8B-Instruct" target="_blank" rel="noopener noreferrer" class="text-blue-400 hover:text-blue-300 underline underline-offset-2 transition-colors">Qwen3-8B-Instruct
Method: LoRA SFT v1 (rank=16, 1 epoch) + v2 continue (0.5 epoch, lr=5e-5)
Training data: MapFinBen train split, unified 51,064 samples
Framework: LLaMA-Factory
Parameters: ~8B (merged full weights)

MapFinBen benchmark: https://github.com/HgITSE/MapFinBen