Instructions to use preparebuddy/ielts-3b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use preparebuddy/ielts-3b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="preparebuddy/ielts-3b-gguf",
	filename="preparebuddy-ielts-3b-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use preparebuddy/ielts-3b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf preparebuddy/ielts-3b-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf preparebuddy/ielts-3b-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf preparebuddy/ielts-3b-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf preparebuddy/ielts-3b-gguf:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf preparebuddy/ielts-3b-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf preparebuddy/ielts-3b-gguf:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf preparebuddy/ielts-3b-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf preparebuddy/ielts-3b-gguf:Q8_0

Use Docker

docker model run hf.co/preparebuddy/ielts-3b-gguf:Q8_0

LM Studio
Jan

vLLM

How to use preparebuddy/ielts-3b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "preparebuddy/ielts-3b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "preparebuddy/ielts-3b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/preparebuddy/ielts-3b-gguf:Q8_0

Ollama
How to use preparebuddy/ielts-3b-gguf with Ollama:
```
ollama run hf.co/preparebuddy/ielts-3b-gguf:Q8_0
```

Unsloth Studio

How to use preparebuddy/ielts-3b-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for preparebuddy/ielts-3b-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for preparebuddy/ielts-3b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for preparebuddy/ielts-3b-gguf to start chatting

How to use preparebuddy/ielts-3b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf preparebuddy/ielts-3b-gguf:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "preparebuddy/ielts-3b-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use preparebuddy/ielts-3b-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf preparebuddy/ielts-3b-gguf:Q8_0

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-3b-gguf:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use preparebuddy/ielts-3b-gguf with Docker Model Runner:
```
docker model run hf.co/preparebuddy/ielts-3b-gguf:Q8_0
```

Lemonade

How to use preparebuddy/ielts-3b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull preparebuddy/ielts-3b-gguf:Q8_0

Run and chat with the model

lemonade run user.ielts-3b-gguf-Q8_0

List all available models

lemonade list

PrepareBuddy IELTS-3B — GGUF (Q8_0)

A GGUF build of PrepareBuddy IELTS-3B, for LM Studio, Ollama, and llama.cpp — runs on Mac, Windows and Linux.

Quantisation: Q8_0 (near-lossless; the model is already 8-bit, so there is no meaningful quality loss vs. the full version). Generates IELTS Academic practice content across all four sections (Reading, Writing, Listening, Speaking).

Full details, examples, supported types, and limitations are on the main model page: 👉 https://huggingface.co/preparebuddy/ielts-3b

Content generator, not an assessment tool. A specialised fine-tune of SmolLM3-3B — not a from-scratch foundation model. Verdict answers (True/False/Not Given) are right most of the time, not always — review before publishing them.

LM Studio

Search preparebuddy/ielts-3b-gguf and download (it loads via the llama.cpp runtime).
Set the 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 structured format, e.g.:

<TEST=IELTS><SECTION=READING><TYPE=MCQ><DIFF=medium><TOPIC=the printing press> Generate a short passage followed by one multiple-choice question (A-D) with an answer key.

Temperature 0.3 for verdict questions (TFNG/YNNG/MCQ), 0.7 for passages/writing/speaking; top_p 0.9.

Ollama

# create a Modelfile pointing at the gguf, set the system prompt + template, then:
ollama create preparebuddy-ielts-3b -f Modelfile
ollama run preparebuddy-ielts-3b

llama.cpp

llama-cli -m preparebuddy-ielts-3b-Q8_0.gguf --temp 0.3 -p "<your structured prompt>"

License

Apache-2.0, inheriting from SmolLM3-3B. Built by PrepareBuddy.

Downloads last month: 9

GGUF

Model size

3B params

Architecture

smollm3

Hardware compatibility

8-bit

Model tree for preparebuddy/ielts-3b-gguf

Base model

HuggingFaceTB/SmolLM3-3B-Base

Finetuned

HuggingFaceTB/SmolLM3-3B

Quantized

(89)

this model