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

Libraries

How to use EigenLabs/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("EigenLabs/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 EigenLabs/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 "EigenLabs/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": "EigenLabs/flint-int4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use EigenLabs/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 "EigenLabs/flint-int4"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "EigenLabs/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": "EigenLabs/flint-int4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use EigenLabs/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 "EigenLabs/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 EigenLabs/flint-int4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use EigenLabs/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 "EigenLabs/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 "EigenLabs/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 / MIRROR_NOTICES.md

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Checkpoint mirror

This package mirrors the complete official RecursiveSI Flint INT4 target checkpoint at revision 3b93f9053b530488405ae9fca51d4900d6ce21f0. Recursive is the model author. Fifteen upstream files, including the weights, tokenizer, chat template, configuration, Apache-2.0 LICENSE and logo, are preserved unchanged. README.md is an EigenLabs-authored model card; the upstream README is preserved byte-for-byte as ORIGINAL_README.md (SHA-256 8041980cec84805c27acd78aa918f3e9d8cadeb0e1668e0841abacb5aea225ac). The README is not a runtime file, so the native runtime aggregate is unchanged.

Upstream: https://huggingface.co/RecursiveSI/flint-int4/tree/3b93f9053b530488405ae9fca51d4900d6ce21f0

The artifact uses mixed-bit affine quantization (INT4 projections/experts, INT8 embedding); it is not NVFP4. It is a text-only, target-only package. No compatible published MTP companion is included. The model chooses when to reason; this checkpoint does not provide trained forced thinking on/off, effort-level or thinking-budget controls.

Native d-inference support credits Prince Canuma / mlx-vlm (MIT) and Recursive / rec (Apache-2.0) in the SDK ThirdPartyNotices/Flint.md, with pinned source revisions and complete notices. Their code is not copied into this model-weight mirror.

This mirror is not a Darkbloom catalog release or a performance qualification. No throughput guarantee is made.