commit b515a1df8f76d5de08c30473efe7f6f0772d1625
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Tue Jun 9 12:06:17 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: Zkare/Chatbot_Ielts_Assistant_v2
    Source: Original Platform

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+---
+language:
+- en
+- vi
+pretty_name: Chatbot IELTS Assistant v2
+license: apache-2.0
+tags:
+- qwen3
+- chatbot
+- conversational
+- ielts
+- education
+- text-generation
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
+---
+
+# 📘 Chatbot IELTS Assistant v2
+
+**Chatbot IELTS Assistant v2** is a fine-tuned conversational language model built on **Qwen3-4B-2507**, designed to assist learners preparing for the **IELTS exam**.  
+It provides natural dialogue responses and helpful explanations for Speaking, Writing, Reading, Listening, vocabulary, and grammar.
+
+---
+
+## 📌 Model Summary
+
+| Attribute        | Value |
+|------------------|-------|
+| **Model type**   | Conversational LLM |
+| **Base model**   | Qwen3-4B-2507 |
+| **Training**     | Fine-tuned for IELTS-related dialogue |
+| **Languages**    | English, Vietnamese |
+| **License**      | Apache-2.0 |
+| **Intended use** | IELTS learning assistant |
+
+---
+
+## 🎯 Intended Use Cases
+
+This model is suitable for:
+
+- IELTS Speaking practice  
+- IELTS Writing task explanations  
+- Vocabulary & grammar guidance  
+- English learning conversation  
+- General educational Q&A  
+
+**NOT recommended for:**
+
+- Legal, medical, financial advice  
+- High-risk decision making  
+- Producing official IELTS scores  
+
+---
+
+## 🚀 How to Use
+
+### Python (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Zkare/Chatbot_Ielts_Assistant_v2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+prompt = "Help me practice IELTS Speaking Part 2."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=180)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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