Instructions to use mmoralesf/mariana-qwen3-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mmoralesf/mariana-qwen3-14b 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 mmoralesf/mariana-qwen3-14b:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf mmoralesf/mariana-qwen3-14b:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mmoralesf/mariana-qwen3-14b:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf mmoralesf/mariana-qwen3-14b:Q5_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 mmoralesf/mariana-qwen3-14b:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf mmoralesf/mariana-qwen3-14b:Q5_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 mmoralesf/mariana-qwen3-14b:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mmoralesf/mariana-qwen3-14b:Q5_K_M

Use Docker

docker model run hf.co/mmoralesf/mariana-qwen3-14b:Q5_K_M

LM Studio
Jan

vLLM

How to use mmoralesf/mariana-qwen3-14b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mmoralesf/mariana-qwen3-14b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mmoralesf/mariana-qwen3-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mmoralesf/mariana-qwen3-14b:Q5_K_M

Ollama
How to use mmoralesf/mariana-qwen3-14b with Ollama:
```
ollama run hf.co/mmoralesf/mariana-qwen3-14b:Q5_K_M
```

Unsloth Studio

How to use mmoralesf/mariana-qwen3-14b 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 mmoralesf/mariana-qwen3-14b 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 mmoralesf/mariana-qwen3-14b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mmoralesf/mariana-qwen3-14b to start chatting

How to use mmoralesf/mariana-qwen3-14b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mmoralesf/mariana-qwen3-14b:Q5_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": "mmoralesf/mariana-qwen3-14b:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mmoralesf/mariana-qwen3-14b with Docker Model Runner:
```
docker model run hf.co/mmoralesf/mariana-qwen3-14b:Q5_K_M
```

Lemonade

How to use mmoralesf/mariana-qwen3-14b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mmoralesf/mariana-qwen3-14b:Q5_K_M

Run and chat with the model

lemonade run user.mariana-qwen3-14b-Q5_K_M

List all available models

lemonade list

Hermes Agent

How to use mmoralesf/mariana-qwen3-14b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mmoralesf/mariana-qwen3-14b:Q5_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 mmoralesf/mariana-qwen3-14b:Q5_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mmoralesf/mariana-qwen3-14b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mmoralesf/mariana-qwen3-14b:Q5_K_M

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 "mmoralesf/mariana-qwen3-14b:Q5_K_M" \
  --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"

Mariana — Qwen3-14B Fine-tuned (Automotive Sales Assistant)

Fine-tuned version of Qwen3-14B for automotive sales conversations in Mexican Spanish.

Model Details

Base model: Qwen/Qwen3-14B
Fine-tuning: LoRA + merged
Language: Spanish (Mexico)
Domain: Automotive sales (used cars)
Tool calling: Hermes format, 11 custom tools

Intended Use

Virtual sales assistant for Autos TREFA, a used car dealership in Mexico. Handles:

Vehicle inventory search and recommendations
Financing calculations
Business information (hours, locations, policies)
Lead capture and follow-up

Available Formats

Format	Path	Use Case
Safetensors (merged)	`/`	vLLM, transformers, TGI
GGUF Q5_K_M	`/gguf/`	llama.cpp, Ollama

Usage with vLLM

vllm serve marianomorales/mariana-qwen3-14b \
  --max-model-len 8192 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

Training

Method: LoRA (r=16, alpha=32)
Dataset: Synthetic automotive sales conversations
Hardware: NVIDIA RTX 5880 Ada (48GB VRAM)

Downloads last month: 42

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for mmoralesf/mariana-qwen3-14b

Base model

Qwen/Qwen3-14B-Base

Finetuned

Qwen/Qwen3-14B

Quantized

(209)

this model

Quantizations

1 model