初始化项目，由ModelHub XC社区提供模型

Model: YorkFr/financial-sentiment-qwen3-v2
Source: Original Platform

README.md

@@ -0,0 +1,61 @@
+---
+base_model:
+- Qwen/Qwen3-0.6B
+license: mit
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+- finance
+- sentiment-analysis
+- lora
+- qwen
+- financial-news
+---
+# 🏦 Qwen3-0.6B - Financial Sentiment Classification (v2)
+
+**Model name:** `YorkFr/financial-sentiment-qwen3-v2`  
+**Base model:** [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)  
+**Fine-tuning type:** LoRA (merged full model)  
+**Task:** Financial news sentiment classification (`positive`, `neutral`, `negative`)
+
+---
+
+## 🧠 Overview
+
+This model is a **fine-tuned version of Qwen3-0.6B**, specialized for **financial sentiment analysis**.
+
+It classifies a short piece of financial or economic news into one of three categories:
+
+- 🟢 `positive` — good market news, growth, profit, increase, etc.  
+- ⚪ `neutral` — balanced or uncertain tone.  
+- 🔴 `negative` — bad market news, loss, risk, decline, etc.
+
+Training was performed using **LoRA (PEFT)** with a small balanced dataset of financial headlines and statements.
+
+---
+
+
+## 💡 Usage
+
+You can load and use the model directly with 🤗 `transformers` — **no PEFT or special tokenizer required.**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("YorkFr/financial-sentiment-qwen3-v2")
+tokenizer = AutoTokenizer.from_pretrained("YorkFr/financial-sentiment-qwen3-v2")
+
+text = (
+    "Instruction: Classify the sentiment of the following financial news sentence "
+    "as one of [positive, neutral, negative].\n"
+    "Sentence: Apple announces strong pre-orders.\n"
+    "Answer:"
+)
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=5)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))