Instructions to use albertobarnabo/fiduciary-qwen3-4b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use albertobarnabo/fiduciary-qwen3-4b-GGUF 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 albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf albertobarnabo/fiduciary-qwen3-4b-GGUF: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 albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf albertobarnabo/fiduciary-qwen3-4b-GGUF: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 albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use albertobarnabo/fiduciary-qwen3-4b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "albertobarnabo/fiduciary-qwen3-4b-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": "albertobarnabo/fiduciary-qwen3-4b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M

Ollama
How to use albertobarnabo/fiduciary-qwen3-4b-GGUF with Ollama:
```
ollama run hf.co/albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M
```

Unsloth Studio

How to use albertobarnabo/fiduciary-qwen3-4b-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 albertobarnabo/fiduciary-qwen3-4b-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 albertobarnabo/fiduciary-qwen3-4b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for albertobarnabo/fiduciary-qwen3-4b-GGUF to start chatting

How to use albertobarnabo/fiduciary-qwen3-4b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf albertobarnabo/fiduciary-qwen3-4b-GGUF: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": "albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use albertobarnabo/fiduciary-qwen3-4b-GGUF with Docker Model Runner:
```
docker model run hf.co/albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M
```

Lemonade

How to use albertobarnabo/fiduciary-qwen3-4b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.fiduciary-qwen3-4b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use albertobarnabo/fiduciary-qwen3-4b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf albertobarnabo/fiduciary-qwen3-4b-GGUF: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 albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use albertobarnabo/fiduciary-qwen3-4b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_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 "albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_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"

fiduciary-qwen3-4b-GGUF

GGUF quants of Fiduciary — a local-first personal-finance advisor LLM (Qwen3-4B-Instruct-2507 fine-tune), for llama.cpp, Ollama, LM Studio, Jan, and everything else in the GGUF universe. Your portfolio questions never leave your machine.

⚠️ Educational tool, not financial advice. Fiduciary explains concepts, reads the portfolio you show it, and talks through trade-offs like a patient advisor — it does not know your full situation and it can be wrong. Decisions are yours.

How do I run a local financial-advisor LLM?

Ollama (fastest path):

ollama run hf.co/albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M

LM Studio: search fiduciary in the model browser, pick Q4_K_M.

llama.cpp:

llama-cli -hf albertobarnabo/fiduciary-qwen3-4b-GGUF:Q4_K_M \
  -p "I'm 28, saving 800€/month. Explain index funds vs picking stocks, for my situation."

Which file should I download?

file	size	use it when
`fiduciary-qwen3-4b-Q4_K_M.gguf`	2.5 GB	default — best size/quality balance, runs on 8 GB RAM
`fiduciary-qwen3-4b-Q5_K_M.gguf`	2.9 GB	a bit more quality headroom, 8–16 GB RAM
`fiduciary-qwen3-4b-Q8_0.gguf`	4.3 GB	near-lossless, 16 GB+ RAM

Need F16 or another quant for re-quantizing? Open a discussion — happy to add it.

What is this model?

A LoRA fine-tune (rank 16, 16 layers, prompt-masked so only advisor turns train) of Qwen3-4B-Instruct-2507, trained with mlx-lm on synthetic advisor dialogues: portfolio review conversations, tool-calling traces (live prices/news), and financial-literacy explanations. Final validation loss 0.046 — a training-sanity signal on a small validation set, not a quality benchmark; no standardized finance benchmark is claimed here.

These GGUFs were made the standard QLoRA way: adapters (trained against the 4-bit MLX base) fused onto the full-precision Qwen3-4B-Instruct-2507, then quantized with llama.cpp b10092. The chat template is embedded.

The full agent runtime — local portfolio.json, price/news tools, the loop that makes it an advisor rather than a chatbot — lives in the GitHub repo. The weights are the brain; the repo is the owl's perch. 🦉

The Fiduciary family

artifact	for
fiduciary-qwen3-4b	Apple silicon / MLX (4-bit, 2.3 GB)
this repo	everyone else: Ollama, LM Studio, llama.cpp, Jan
fiduciary-qwen3-4b-lora	the raw LoRA adapter (56 MB) — stack it on your own Qwen3-4B

Limitations

English-first; finance terminology skews US/EU retail investing.
A 4B model: confidently wrong sometimes — verify numbers before acting.
Knowledge is frozen at base-model training; live data comes from the agent tools, not the weights.
Trained on synthetic dialogues (generated + curated), not real client conversations.

Downloads last month: 89

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for albertobarnabo/fiduciary-qwen3-4b-GGUF

Base model

Qwen/Qwen3-4B-Instruct-2507

Adapter

albertobarnabo/fiduciary-qwen3-4b

Quantized

(1)

this model