Instructions to use simpledirect/Vinci-Prova-7B-1.0-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 simpledirect/Vinci-Prova-7B-1.0-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 simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf simpledirect/Vinci-Prova-7B-1.0-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 simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf simpledirect/Vinci-Prova-7B-1.0-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 simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf simpledirect/Vinci-Prova-7B-1.0-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 simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use simpledirect/Vinci-Prova-7B-1.0-GGUF with Ollama:
```
ollama run hf.co/simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M
```
Unsloth Desktop

How to use simpledirect/Vinci-Prova-7B-1.0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf simpledirect/Vinci-Prova-7B-1.0-GGUF: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": "simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use simpledirect/Vinci-Prova-7B-1.0-GGUF with Docker Model Runner:
```
docker model run hf.co/simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M
```

Lemonade

How to use simpledirect/Vinci-Prova-7B-1.0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vinci-Prova-7B-1.0-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use simpledirect/Vinci-Prova-7B-1.0-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 simpledirect/Vinci-Prova-7B-1.0-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 simpledirect/Vinci-Prova-7B-1.0-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use simpledirect/Vinci-Prova-7B-1.0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf simpledirect/Vinci-Prova-7B-1.0-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 "simpledirect/Vinci-Prova-7B-1.0-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"

Vinci Prova 7B 1.0 - GGUF

Quantised GGUF builds of simpledirect/Vinci-Prova-7B-1.0.

file	quantisation	sha256
`Vinci-Prova-7B-1.0-f16.gguf`	f16 (unquantised conversion)	`195460f2f2fbf033f5bee226903a9ab51591d26f62f6cd83702867e5d66f9e03`
`Vinci-Prova-7B-1.0-Q4_K_M.gguf`	Q4_K_M	`655886167ea8f07877954ee949b205557eb810774860535b84aa41c3446ca750`
`Vinci-Prova-7B-1.0-Q5_K_M.gguf`	Q5_K_M	`ed521b15992b6716042e6bae4a9785bd1827ec05bf99661473adeb035c830f1b`
`Vinci-Prova-7B-1.0-Q8_0.gguf`	Q8_0	`b0b724b4dd77d132f47ed364fed7053fc0dfa30edb4839cc07212ec0f206b9dc`

Verify what you downloaded against these hashes. Equal file size is not identity.

These files are not the measured artifact

🔴 No GGUF file in this repository has been evaluated. Every published measurement was taken on the unquantised weights in the main repo, not on any file here. Quantisation changes model behaviour, and with no per-tier measurement this card cannot say in which direction or by how much for any tier.

This card therefore publishes no per-tier score, no quality ranking between the tiers, and no recommended tier. A file's presence in this repository is not a performance claim about that tier, and not a claim of parity with the unquantised weights.

All evaluation results, limitations, intended use and provenance live on the main repo. Read its model card before using these builds. Nothing is restated here, so there is one place for a number to be correct. The full study is published as Vinci Technical Report No. 1 (CC BY 4.0).

Follow-up, 1 September 2026. Technical Report No. 2 applied the same frozen recipe to three model families and did not establish general cross-lineage portability. The Report No. 1 measurements are unchanged. Both reports are summarised on the main repo's card; nothing is restated here.