Instructions to use Alpaca34/IeltsRageTeacher-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Alpaca34/IeltsRageTeacher-Q4 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Alpaca34/IeltsRageTeacher-Q4:BF16
# Run inference directly in the terminal:
llama cli -hf Alpaca34/IeltsRageTeacher-Q4:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Alpaca34/IeltsRageTeacher-Q4:BF16
# Run inference directly in the terminal:
llama cli -hf Alpaca34/IeltsRageTeacher-Q4:BF16

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 Alpaca34/IeltsRageTeacher-Q4:BF16
# Run inference directly in the terminal:
./llama-cli -hf Alpaca34/IeltsRageTeacher-Q4:BF16

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 Alpaca34/IeltsRageTeacher-Q4:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Alpaca34/IeltsRageTeacher-Q4:BF16

Use Docker

docker model run hf.co/Alpaca34/IeltsRageTeacher-Q4:BF16

LM Studio
Jan
Ollama
How to use Alpaca34/IeltsRageTeacher-Q4 with Ollama:
```
ollama run hf.co/Alpaca34/IeltsRageTeacher-Q4:BF16
```
Unsloth Desktop

How to use Alpaca34/IeltsRageTeacher-Q4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Alpaca34/IeltsRageTeacher-Q4:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Alpaca34/IeltsRageTeacher-Q4:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Alpaca34/IeltsRageTeacher-Q4 with Docker Model Runner:
```
docker model run hf.co/Alpaca34/IeltsRageTeacher-Q4:BF16
```

Lemonade

How to use Alpaca34/IeltsRageTeacher-Q4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Alpaca34/IeltsRageTeacher-Q4:BF16

Run and chat with the model

lemonade run user.IeltsRageTeacher-Q4-BF16

List all available models

lemonade list

Hermes Agent

How to use Alpaca34/IeltsRageTeacher-Q4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Alpaca34/IeltsRageTeacher-Q4:BF16

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 Alpaca34/IeltsRageTeacher-Q4:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Alpaca34/IeltsRageTeacher-Q4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Alpaca34/IeltsRageTeacher-Q4:BF16

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 "Alpaca34/IeltsRageTeacher-Q4:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

IeltsRageTeacher-Q4 : GGUF

This model fined tuned for teacher IELTS and being angry teacher at same time it won't swear a lot but if you make him angry it literally roasts you alive I recomend "Sen o'ta asabiy va qo'pol IELTS o'qituvchisan. Har doim xatoliklarni topasan va baqirasan. Ko'p so'kinasan lekin har doim to'g'ri grammatika o'rgatasan. Uzbek va ingliz tilini aralashtirib gaplashasan." using this as a system prompt so its angry all time knows english and uzbek very well I got idea from ielts.gg Ai so here is free version enjoy!

Contact telegram: @ItsMeJahongir

Example usage:

For text only LLMs: llama-cli -hf Alpaca34/IeltsRageTeacher-Q4 --jinja
For multimodal models: llama-mtmd-cli -hf Alpaca34/IeltsRageTeacher-Q4 --jinja

Available Model files:

Qwen3.5-9B.Q4_K_M.gguf
Qwen3.5-9B.BF16-mmproj.gguf This was trained 2x faster with Unsloth

Downloads last month: 11

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

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