Instructions to use RecursiveSI/flint-int4-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 RecursiveSI/flint-int4-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 RecursiveSI/flint-int4-gguf:Q4_1
# Run inference directly in the terminal:
llama cli -hf RecursiveSI/flint-int4-gguf:Q4_1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RecursiveSI/flint-int4-gguf:Q4_1
# Run inference directly in the terminal:
llama cli -hf RecursiveSI/flint-int4-gguf:Q4_1

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 RecursiveSI/flint-int4-gguf:Q4_1
# Run inference directly in the terminal:
./llama-cli -hf RecursiveSI/flint-int4-gguf:Q4_1

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 RecursiveSI/flint-int4-gguf:Q4_1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RecursiveSI/flint-int4-gguf:Q4_1

Use Docker

docker model run hf.co/RecursiveSI/flint-int4-gguf:Q4_1

LM Studio
Jan

vLLM

How to use RecursiveSI/flint-int4-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RecursiveSI/flint-int4-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": "RecursiveSI/flint-int4-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RecursiveSI/flint-int4-gguf:Q4_1

Ollama
How to use RecursiveSI/flint-int4-gguf with Ollama:
```
ollama run hf.co/RecursiveSI/flint-int4-gguf:Q4_1
```
Unsloth Desktop

How to use RecursiveSI/flint-int4-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RecursiveSI/flint-int4-gguf:Q4_1

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": "RecursiveSI/flint-int4-gguf:Q4_1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use RecursiveSI/flint-int4-gguf with Docker Model Runner:
```
docker model run hf.co/RecursiveSI/flint-int4-gguf:Q4_1
```

Lemonade

How to use RecursiveSI/flint-int4-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RecursiveSI/flint-int4-gguf:Q4_1

Run and chat with the model

lemonade run user.flint-int4-gguf-Q4_1

List all available models

lemonade list

Hermes Agent

How to use RecursiveSI/flint-int4-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 RecursiveSI/flint-int4-gguf:Q4_1

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 RecursiveSI/flint-int4-gguf:Q4_1

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use RecursiveSI/flint-int4-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RecursiveSI/flint-int4-gguf:Q4_1

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 "RecursiveSI/flint-int4-gguf:Q4_1" \
  --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"

Flint INT4 GGUF

Q4_1 GGUF weights for Recursive Flint, primarily intended for Apple Silicon using llama.cpp with Metal. The 26.0 GB file includes weights, tokenizer, and chat template.

See Flint’s model card for model details, benchmarks, chat conventions, and limitations.

Run on Apple Silicon

Requires an Apple Silicon Mac with at least 48 GB of unified memory.

Follow llama.cpp’s Metal installation/build guide, using a release with Flint support, and put llama-server on your PATH. Use Metal GPU offload with -ngl all.

With the Hugging Face CLI installed:

hf download RecursiveSI/flint-int4-gguf flint-int4-q4_1.gguf pi-models.json opencode.json \
  --local-dir flint-int4-gguf

llama-server -m flint-int4-gguf/flint-int4-q4_1.gguf \
  --alias flint -ngl all -c 262144 -np 1 --cache-ram 0 \
  -b 256 -ub 128 -t 8 -fa on -ctk f16 -ctv f16 --jinja \
  --temp 1 --top-p 0.95 --top-k 20 --min-p 0 \
  --host 127.0.0.1 --port 8080

The example uses the full 262,144-token context with F16 KV. Reduce -c only if you need to lower KV memory use. Leave --swa-full unset and retain --cache-ram 0 to limit extra host-memory use. No sliding-window or RoPE override is needed.

Use with pi or OpenCode

Keep llama-server running and open another terminal in the directory containing the downloaded model folder.

pi

Install pi using its installation guide, then run:

mkdir -p .pi-flint
cp flint-int4-gguf/pi-models.json .pi-flint/models.json
PI_CODING_AGENT_DIR="$PWD/.pi-flint" pi --provider flint-local --model flint

See pi’s custom-model guide for configuration options.

OpenCode

Install OpenCode using its installation guide, then run:

OPENCODE_CONFIG="$PWD/flint-int4-gguf/opencode.json" opencode --standalone

See OpenCode’s provider guide for configuration options.

The client configs use 262,144 context tokens and a configurable 65,536-token generation budget, including reasoning. Prompt and output share the total context. If you change -c, match the client context limit to it.

Format notes

Q4_1 stores the MLX export’s trained INT4 codes with FP16 scales and offsets. Embeddings and other floating tensors are stored as F32, making this file larger than Flint INT4.

Use --jinja and preserve reasoning_content when replaying conversation history.

Training data

Flint’s training data included publicly available and crawled web documents, text and code, commercially licensed data, and AI-generated synthetic data. Post-training included instruction tuning, reinforcement learning and human-preference data. See the base Flint model card for shared model and training information.

License

Apache License 2.0.

Downloads last month: -

GGUF

Model size

40B params

Architecture

flint

Hardware compatibility

4-bit