metadata
license: apache-2.0
base_model: Qwen/Qwen3-8B-Instruct
tags:
- finance
- multilingual
- mapfinben
- qwen3
- lora
language:
- en
- zh
- id
- es
- el
- ja
mapfinben-qwen3-merged-unified-v2
Unified LoRA fine-tuned Qwen3-8B-Instruct for CCL26-Eval-MapFinBen (v2 continued fine-tune on v1 adapter).
Model Details
- Base model: 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)
Usage
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
MapFinBen benchmark: https://github.com/HgITSE/MapFinBen