Instructions to use RecursiveSI/flint-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RecursiveSI/flint-int4 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("RecursiveSI/flint-int4")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use RecursiveSI/flint-int4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "RecursiveSI/flint-int4"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "RecursiveSI/flint-int4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use RecursiveSI/flint-int4 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "RecursiveSI/flint-int4"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "RecursiveSI/flint-int4"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "RecursiveSI/flint-int4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use RecursiveSI/flint-int4 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "RecursiveSI/flint-int4"

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use RecursiveSI/flint-int4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "RecursiveSI/flint-int4"

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" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

flint-int4

File size: 1,621 Bytes

---
license: apache-2.0
pipeline_tag: text-generation
tags:
- mlx
- int4
- safetensors
---

<p align="center">
  <img src="assets/flint-logo.png" width="160" alt="Flint logo">
</p>

<h1 align="center">Flint INT4</h1>

MLX-format INT4 weights for [Recursive Flint](https://huggingface.co/RecursiveSI/flint), primarily intended for local inference on Apple Silicon. Projections and experts use **group-64 affine INT4**; embeddings use INT8. The weights occupy approximately **22.6 GB**.

See [Flint’s model card](https://huggingface.co/RecursiveSI/flint) for model details, benchmarks, chat conventions, and limitations.

## Use with Rec

Follow [Rec’s requirements and installation guide](https://github.com/recursive-oss/rec), then launch pi or OpenCode:

```bash
rec launch pi
# Or use OpenCode:
rec launch opencode
```

## Download the weights separately

With the [Hugging Face CLI](https://huggingface.co/docs/huggingface_hub/guides/cli) installed:

```bash
hf download RecursiveSI/flint-int4 --local-dir flint-int4
```

For llama.cpp on Metal, use [Flint INT4 GGUF](https://huggingface.co/RecursiveSI/flint-int4-gguf).

## 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](https://huggingface.co/RecursiveSI/flint#training) for shared model and training information.

## License

[Apache License 2.0](https://huggingface.co/RecursiveSI/flint-int4/blob/main/LICENSE).