Instructions to use netprojects/h2o-lightning-4b-mlx-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use netprojects/h2o-lightning-4b-mlx-q4 with MLX:

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

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("netprojects/h2o-lightning-4b-mlx-q4")
config = load_config("netprojects/h2o-lightning-4b-mlx-q4")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use netprojects/h2o-lightning-4b-mlx-q4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "netprojects/h2o-lightning-4b-mlx-q4"

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": "netprojects/h2o-lightning-4b-mlx-q4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use netprojects/h2o-lightning-4b-mlx-q4 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 "netprojects/h2o-lightning-4b-mlx-q4"

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 netprojects/h2o-lightning-4b-mlx-q4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use netprojects/h2o-lightning-4b-mlx-q4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "netprojects/h2o-lightning-4b-mlx-q4"

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 "netprojects/h2o-lightning-4b-mlx-q4" \
  --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"

H2O-Lightning-4B — MLX 4-bit

A 4-bit MLX conversion (affine, group size 64; mlx-vlm 0.7.6) of h2oai/h2o-lightning-4b v1.2.3, for Apple Silicon. 3.0 GB.

Published by NetProjects (Internet Projects Ltd). Not affiliated with H2O.ai.

This is a decision model, not a chat model. It returns a probability for each answer option, not text.

Serve it with the H2O shim, not with mlx_lm.server or a plain mlx_vlm generate call. The generic "Use this model" snippets Hugging Face shows for MLX models do not return the label probabilities this model is made for. The commands below set it up with H2O's official shim, unmodified, on top of h2o-lightning-mlx, which reads the label log-probabilities with a float32 head, as the original does on vLLM.

JevBench public (231, CLI `bb05a33`, `--adapter typesafe`)
this checkpoint	207 / 231 (48/48 · 72/72 · 87/111)
8-bit build	205 / 231 (q8)
original weights on MLX (bf16)	204 / 231
vs bf16 on MLX	220 / 231 answers identical; probability drift mean 0.039, p95 0.179

The higher score is noise (11 answers flip both ways) and calibration is visibly looser. Prefer the 8-bit build unless memory is tight.

Images. Image input works through the unchanged shim. On H2O's own receipt example this build gives the same answers as the model card shows. In a small generated-image check (counting, colours, reading a table) it was slightly weaker at counting objects (28 of 30 against 30 of 30 for the 8-bit build). It is not a reproduction of H2O's image benchmark.

Limits (as for the original): mostly evaluated in English; works best with about 16 answer options or fewer; no explanations and no video input.

git clone https://github.com/internet-projects/h2o-lightning-mlx && cd h2o-lightning-mlx && ./setup.sh
hf download netprojects/h2o-lightning-4b-mlx-q4 --local-dir mlx-q4
MODEL=mlx-q4 ./serve.sh # http://127.0.0.1:8741/v1/systemone

Tested with the shim at H2O revision v1.2.3.

Full precision: use the original weights, h2oai/h2o-lightning-4b, with the same repo and shim. There is no separate bf16 copy here.

Credits: H2O.ai (H2O-Lightning-4B, Apache-2.0), Qwen team (Qwen3.5-4B, Apache-2.0). This conversion is not affiliated with H2O.ai.

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Safetensors

Model size

5B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for netprojects/h2o-lightning-4b-mlx-q4

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

h2oai/h2o-lightning-4b

Quantized

(6)

this model