| | --- |
| | 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)) |
