Instructions to use preparebuddy/ielts-4b-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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

OpenClaw new

How to use preparebuddy/ielts-4b-mlx with OpenClaw:

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 OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "preparebuddy/ielts-4b-mlx" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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"}
     ]
   }'

ielts-4b-mlx / README.md

preparebuddy

IELTS-4B MLX 4-bit (Apple Silicon / LM Studio)

2f44e1e verified about 1 month ago

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metadata

license: apache-2.0
base_model: Qwen/Qwen3.5-4B
library_name: mlx
pipeline_tag: text-generation
language:
  - en
tags:
  - ielts
  - education
  - content-generation
  - mlx

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-mlx and 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.