license: apache-2.0
language:
- en
base_model: Qwen/Qwen3-4B-Instruct-2507
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
- apple-silicon
- qwen3
- finance
- financial-advisor
- financial-literacy
- personal-finance
- local-llm
- privacy
- self-hosted
- lora
Nota sui parametri: questo repo contiene i pesi Qwen3-4B quantizzati 4-bit via MLX (uint32 packing). Il contatore di parametri di Hugging Face legge i tensori impacchettati e mostra un valore errato: il modello ha 4B parametri, non 0.6B.
fiduciary-qwen3-4b 🦉
A personal-finance advisor LLM that lives on your Mac — not in someone else's cloud. This is the MLX 4-bit build (2.3 GB) of Fiduciary, a Qwen3-4B-Instruct-2507 fine-tune for Apple silicon. It reads the portfolio you keep in a local JSON file, explains investing concepts like a patient senior advisor, and never sends a byte of your finances anywhere.
⚠️ Educational tool, not financial advice. Fiduciary explains and discusses; it does not know your full situation and can be wrong. Decisions are yours.
Not on a Mac? Use the GGUF build
for Ollama / LM Studio / llama.cpp. This repo's weights are MLX-quantized and load
with mlx-lm — they will not load with plain 🤗 transformers.
Why would I run a financial advisor locally?
Because the alternative is uploading your portfolio, your balances, and your money
anxieties to an API. A 4B model on your own machine is private by construction:
you own the weights (Apache-2.0), you own the data (a plain portfolio.json on
disk), and you can read every tool it calls in the
GitHub repo — the agent runtime
that fetches live prices and news lives there, in auditable Python, not in the weights.
How do I run it on my Mac?
pip install mlx-lm
mlx_lm.chat --model albertobarnabo/fiduciary-qwen3-4b
from mlx_lm import load, generate
model, tokenizer = load("albertobarnabo/fiduciary-qwen3-4b")
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "I'm 28 and can save 800€/month. Walk me through index funds vs stock picking for someone like me."}],
add_generation_prompt=True, tokenize=False)
print(generate(model, tokenizer, prompt, max_tokens=512))
For the full advisor experience (portfolio file, live prices, news) clone the runtime repo and follow its README.
How was it trained?
LoRA (rank 16, first 16 layers) on Qwen3-4B-Instruct-2507 with mlx-lm on a
16 GB MacBook — the whole fine-tune ran on consumer Apple silicon. Data: synthetic
advisor dialogues (portfolio reviews, tool-calling traces, financial-literacy
explanations), generated and curated in the runtime repo; mask_prompt: true so
only advisor turns contribute to the loss, expanded per assistant turn. Final
validation loss 0.046 — read that as a training-sanity signal on a small
validation set, not a benchmark; this model makes no benchmark claims.
The Fiduciary family
| artifact | for |
|---|---|
| this repo | Apple silicon via MLX (4-bit, 2.3 GB) |
| fiduciary-qwen3-4b-GGUF | Ollama, LM Studio, llama.cpp, Jan — every other machine |
| fiduciary-qwen3-4b-lora | the raw LoRA adapter (56 MB) |
Limitations
- English-first; terminology skews US/EU retail investing.
- 4B parameters: articulate, occasionally confidently wrong — verify numbers.
- Weights know nothing after the base model's cutoff; live market data comes from the runtime's tools, not the model.
- Trained on synthetic dialogues, not real client conversations — and it shows in breadth: niche instruments and tax specifics are weak spots.