Instructions to use preparebuddy/ielts-4b-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use preparebuddy/ielts-4b-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("preparebuddy/ielts-4b-mlx") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use preparebuddy/ielts-4b-mlx with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "preparebuddy/ielts-4b-mlx"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "preparebuddy/ielts-4b-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use preparebuddy/ielts-4b-mlx with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "preparebuddy/ielts-4b-mlx"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default preparebuddy/ielts-4b-mlx
Run Hermes
hermes
- MLX LM
How to use preparebuddy/ielts-4b-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "preparebuddy/ielts-4b-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "preparebuddy/ielts-4b-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "preparebuddy/ielts-4b-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
PrepareBuddy IELTS-4B — MLX (Apple Silicon)
The MLX 4-bit build of PrepareBuddy IELTS-4B — the recommended, best-balanced model in the family — for fast local use on Apple Silicon Macs, including LM Studio. Generates IELTS Academic practice across all four sections.
Content generator, not an assessment tool. A fine-tune of Qwen3.5-4B — not a foundation model. Full details, examples, the 2B/4B/9B comparison, and the grounding+verification how-to: 👉 https://huggingface.co/preparebuddy/ielts-4b
Run in LM Studio (Mac)
- Search
preparebuddy/ielts-4b-mlxand download it. - System prompt:
You generate authentic IELTS Academic practice content across reading, writing, listening, and speaking. Produce passages, transcripts, tasks, questions, and answer keys or model answers as appropriate to the section. Use IELTS-style register: academic, neutral, factually plausible. This is content generation, not assessment.
- Prompt with the tag format, e.g.:
<TEST=IELTS><SECTION=READING><TYPE=TFNG><DIFF=medium><TOPIC=solar power> Generate a short passage with 4 True/False/Not Given statements and an answer key. - Settings: temperature 0.3 for verdicts (TFNG/YNNG/MCQ), 0.7 for passages/writing/speaking; top_p 0.9. Max tokens ~600.
Tip: for reliable verdict answer keys, generate against a real passage and use the re-checking loop (see the main page).
License
Apache-2.0, inheriting from Qwen3.5-4B. Built by PrepareBuddy.
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