Upload MapFinBen unified merged Qwen3-8B v2 model: README.md

README.md

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+---
+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](https://huggingface.co/Qwen/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
+
+```python
+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