初始化项目，由ModelHub XC社区提供模型

Model: LeeChanRX/LeeChanRX-3B-Instruct
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- chat
+- instruct
+- conversational
+- fine-tuned
+- leechanrx
+- assistant
+---
+
+# 🧠 LeeChan-3B-Instruct
+
+LeeChan-3B-Instruct is a conversational AI assistant model created and fine-tuned by LeeChanRX.
+
+Built on top of Qwen2.5-3B-Instruct, this model is designed to provide natural conversations, helpful responses, coding assistance, and instruction-following behavior with a friendly and stable personality.
+
+The model has been customized to act as “LeeChan”, an intelligent and conversational AI assistant focused on clarity, reliability, and user-friendly interaction.
+
+---
+
+# ✨ Features
+
+- Conversational AI assistant
+- Instruction-following optimized
+- Coding and programming support
+- Friendly and natural responses
+- Stable chat behavior
+- Fine-tuned personality alignment
+- Lightweight 3B parameter architecture
+- Transformers compatible
+- Standalone merged model
+
+---
+
+# 🏗️ Base Model
+
+This model is fine-tuned from:
+
+Qwen/Qwen2.5-3B-Instruct
+
+Credits and appreciation go to the original Qwen team for providing the open-source foundation model.
+
+---
+
+# 🚀 Usage
+
+## Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "LeeChanRX/LeeChan-3B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+messages = [
+    {
+        "role": "system",
+        "content": "You are LeeChan, a helpful AI assistant."
+    },
+    {
+        "role": "user",
+        "content": "Hello"
+    }
+]
+
+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=128,
+    temperature=0.7,
+    repetition_penalty=1.1
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))