Instructions to use Rafaelcedav/ai-coach with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rafaelcedav/ai-coach with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Rafaelcedav/ai-coach",
	filename="ai-coach-Q4_K_M.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 Rafaelcedav/ai-coach with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rafaelcedav/ai-coach:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Rafaelcedav/ai-coach:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rafaelcedav/ai-coach:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Rafaelcedav/ai-coach:Q4_K_M

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 Rafaelcedav/ai-coach:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Rafaelcedav/ai-coach:Q4_K_M

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 Rafaelcedav/ai-coach:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Rafaelcedav/ai-coach:Q4_K_M

Use Docker

docker model run hf.co/Rafaelcedav/ai-coach:Q4_K_M

LM Studio
Jan

vLLM

How to use Rafaelcedav/ai-coach with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Rafaelcedav/ai-coach:Q4_K_M

Ollama
How to use Rafaelcedav/ai-coach with Ollama:
```
ollama run hf.co/Rafaelcedav/ai-coach:Q4_K_M
```

Unsloth Studio

How to use Rafaelcedav/ai-coach 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 Rafaelcedav/ai-coach 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 Rafaelcedav/ai-coach to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Rafaelcedav/ai-coach to start chatting

How to use Rafaelcedav/ai-coach with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Rafaelcedav/ai-coach:Q4_K_M

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": "Rafaelcedav/ai-coach:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Rafaelcedav/ai-coach with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Rafaelcedav/ai-coach:Q4_K_M

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 Rafaelcedav/ai-coach:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Rafaelcedav/ai-coach with Docker Model Runner:
```
docker model run hf.co/Rafaelcedav/ai-coach:Q4_K_M
```

Lemonade

How to use Rafaelcedav/ai-coach with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rafaelcedav/ai-coach:Q4_K_M

Run and chat with the model

lemonade run user.ai-coach-Q4_K_M

List all available models

lemonade list

AI Course Architect — Qwen2.5-3B Fine-tuned

Modelo especializado en generación de roadmaps pedagógicos estructurados en JSON. Fine-tuning de Qwen2.5-3B-Instruct con QLoRA sobre dataset propietario de diseño instruccional.

Uso

El modelo recibe un perfil de creador de curso y responde únicamente con JSON válido:

\EXPERTISE: Marketing digital AUDIENCE: Emprendedores principiantes PAIN: No se por donde empezar OUTCOME: Lanzar mi primer curso en linea TIME: 5 horas/semana DURATION: 8 semanas FORMAT: Video + ejercicios PLATFORM: Teachable \

Specs de entrenamiento

Parametro	Valor
Base model	Qwen/Qwen2.5-3B-Instruct
Metodo	QLoRA 4-bit (BitsAndBytes)
Rank	r=64, alpha=128
Learning rate	2e-4
Epochs	3
Steps	270
Train loss final	~0.25
Plataforma	Kaggle T4

Archivos

Archivo	Descripcion
i-coach-Q4_K_M.gguf	GGUF cuantizado Q4_K_M — listo para Ollama/LM Studio

Carga rapida con Ollama

\ash

Modelfile

FROM ai-coach-Q4_K_M.gguf PARAMETER temperature 0.3 PARAMETER num_ctx 4096

ollama create ai-coach -f Modelfile ollama run ai-coach \

Parte del stack Epoch Forge

Desarrollado como Fase 1 del proyecto AI Course Architect. Target de produccion: iPhone 17 Pro (Core ML) + Ryzen 3 (Ollama API).

Downloads last month: 10

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Rafaelcedav/ai-coach

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(238)

this model