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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use firela-ai/firela-pa-pc 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 firela-ai/firela-pa-pc:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf firela-ai/firela-pa-pc:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf firela-ai/firela-pa-pc:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf firela-ai/firela-pa-pc: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 firela-ai/firela-pa-pc:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf firela-ai/firela-pa-pc: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 firela-ai/firela-pa-pc:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf firela-ai/firela-pa-pc:Q4_K_M

Use Docker

docker model run hf.co/firela-ai/firela-pa-pc:Q4_K_M

LM Studio
Jan
Ollama
How to use firela-ai/firela-pa-pc with Ollama:
```
ollama run hf.co/firela-ai/firela-pa-pc:Q4_K_M
```
Unsloth Desktop

How to use firela-ai/firela-pa-pc with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf firela-ai/firela-pa-pc:Q4_K_M

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": "firela-ai/firela-pa-pc:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use firela-ai/firela-pa-pc with Docker Model Runner:
```
docker model run hf.co/firela-ai/firela-pa-pc:Q4_K_M
```

Lemonade

How to use firela-ai/firela-pa-pc with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull firela-ai/firela-pa-pc:Q4_K_M

Run and chat with the model

lemonade run user.firela-pa-pc-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use firela-ai/firela-pa-pc with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf firela-ai/firela-pa-pc: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 firela-ai/firela-pa-pc:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use firela-ai/firela-pa-pc with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf firela-ai/firela-pa-pc: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 "firela-ai/firela-pa-pc: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"

firela-pa PC — a Sovereign Personal AI FIRE advisor on your own device

firela-pa is a Sovereign Personal AI — a FIRE financial advisor that lives on your own device: your device decides what ever leaves, its memory of you never leaves home, and the cloud is used only when needed — with the question redacted, the provider swappable, and your identity never on the cloud side.

Sovereign Personal AI = personal service × three user-held rights × zero-tracking foundation

Classification right — a local, fine-tuned router on your device decides what leaves, not an OS vendor

Memory right — voice and agent memory never leave the device; transaction-level ledger data never goes to the cloud (aggregates only, under a field-level egress policy)

Cloud-leg right — anything that goes to the cloud passes a redaction gateway first, and the provider is swappable at any time

What this is

The PC distribution of firela-pa: a task-fine-tuned Qwen3-1.7B privacy router (five intents: ledger query / portfolio / market quote / local chat / redacted-cloud), shipped as GGUF (Q4_K_M default, Q8_0 quality tier; 200-question eval G4 98.0) — the same weights family as the on-board W8A8 build.

v1.0 capabilities (2026-09-24 stable)

Five-branch local routing — what leaves the device is decided by a router on your device, not by an OS vendor. Chat, ledger, portfolio and market questions stay local or go through your own token; only advice-type questions go to the cloud — redacted first.
Redaction gateway — CN ID/phone/address/person names, SSN, US phone, Japan My Number and email are scrubbed from the question before any cloud call; the provider is swappable.
Local agent memory — FIRE assumptions and goals in a local SQLite store (0600), saved only on explicit "remember this", never leaving your device.
FIRE simulation engine — real-return compounding, withdrawal-rate sensitivity sweep, and historical stress-sequence replay (US stock/bond rolling windows 1928–2025, worst-start 1966, window survival rates) — in the free tier, not behind a paid SaaS.
Proactive audit — overspend / large-expense / FIRE-milestone checks over your own ledger, rendered locally.
Local-only telemetry — a local SQLite diary (0600) for your own instrument panel. It never uploads anything.

Install (macOS / Linux / WSL2)

bash <(curl -fsSL https://huggingface.co/firela-ai/firela-pa-pc/resolve/main/install.sh)

Automatically: installs/starts Ollama (version gate ≥0.34) → downloads the app and router model from this repo (sha256-verified) → registers firela-router → pulls the generation model qwen2.5:3b-instruct (--no-gen to skip) → interactive 0600 config (vlt / relay credentials, may be left blank) → Time Machine exclusion → firela-pa command → smoke test. Unattended clean-machine install measured at ~90s.

Privacy boundary: inference never leaves home — routing and chat generation are fully local; ledger queries go through your own vlt token (zero-tracking foundation: the cloud side sees data, never you); cloud-bound questions are redacted first and carry only aggregate ledger data (field-level egress policy — transaction details never go to the cloud).

File	Purpose
`pc-app.tar.gz`	orchestrator + redaction gateway + simulation engine + memory + audit (pure-stdlib Python, 14 files)
`router-merged-Q4_K_M.gguf`	router model, default tier (1.1 GB)
`router-merged-Q8_0.gguf`	router model, quality tier (1.9 GB, optional)
`install.sh`	one-line installer

Source code (Apache-2.0, v1.0 snapshot + single-egress CI gate): https://github.com/firela-ai/firela-pa

License & attribution

Everything in this repo — pc-app.tar.gz, install.sh, router-merged-Q4_K_M.gguf, router-merged-Q8_0.gguf — is provided under Apache License 2.0.

firela-pa is an AI tool, not a licensed financial advisor. Simulation results are historical scenario analyses, not predictions.

Downloads last month: 163

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit

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