Instructions to use majentik/gemma-4-31B-it-RotorQuant-MLX-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majentik/gemma-4-31B-it-RotorQuant-MLX-2bit 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("majentik/gemma-4-31B-it-RotorQuant-MLX-2bit")
config = load_config("majentik/gemma-4-31B-it-RotorQuant-MLX-2bit")

# 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 majentik/gemma-4-31B-it-RotorQuant-MLX-2bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/gemma-4-31B-it-RotorQuant-MLX-2bit"

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": "majentik/gemma-4-31B-it-RotorQuant-MLX-2bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use majentik/gemma-4-31B-it-RotorQuant-MLX-2bit 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 "majentik/gemma-4-31B-it-RotorQuant-MLX-2bit"

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 majentik/gemma-4-31B-it-RotorQuant-MLX-2bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use majentik/gemma-4-31B-it-RotorQuant-MLX-2bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/gemma-4-31B-it-RotorQuant-MLX-2bit"

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 "majentik/gemma-4-31B-it-RotorQuant-MLX-2bit" \
  --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"

majentik commited on 10 days ago

Commit

ee05058

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1 Parent(s): 2861d44

Remove links to retired duplicate brand variants

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README.md +0 -6

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@@ -96,7 +96,6 @@ upstream options described above (`-ctk/-ctv q8_0`, `OLLAMA_KV_CACHE_TYPE`).
 | Precision | Approximate Size | MLX Variant |
 |---|---|---|
 | FP16 (original) | ~62 GB | -- |
-| 8-bit quantized | ~31 GB | [RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-31B-it-RotorQuant-MLX-8bit) |
 | 4-bit quantized | ~17 GB | [RotorQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-31B-it-RotorQuant-MLX-4bit) |
 | **2-bit quantized** | **~9 GB** | **This model** |
@@ -112,9 +111,7 @@ This model requires approximately 9 GB of unified memory. Recommended hardware:
 ## See Also
 - [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) -- Base model
-- [majentik/gemma-4-31B-it-RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-31B-it-RotorQuant-MLX-8bit) -- MLX 8-bit variant
 - [majentik/gemma-4-31B-it-RotorQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-31B-it-RotorQuant-MLX-4bit) -- MLX 4-bit variant
-- [majentik/gemma-4-31B-it-TurboQuant-MLX-2bit](https://huggingface.co/majentik/gemma-4-31B-it-TurboQuant-MLX-2bit) -- TurboQuant MLX 2-bit variant
 - [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)
 - [MLX Framework](https://github.com/ml-explore/mlx)
@@ -145,7 +142,4 @@ This model requires approximately 9 GB of unified memory. Recommended hardware:
 | [RotorQuant-GGUF-Q8_0](https://huggingface.co/majentik/gemma-4-31b-it-rotorquant-gguf-Q8_0) | llama.cpp | ~65 GB | Near-lossless reference |
 | **RotorQuant-MLX-2bit** | mlx-lm | ~9.9 GB | Apple Silicon, smallest |
 | [RotorQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-31b-it-rotorquant-mlx-4bit) | mlx-lm | ~19 GB | Apple Silicon balanced |
-| [RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-31b-it-rotorquant-mlx-8bit) | mlx-lm | ~37 GB | Apple Silicon reference |
-| [TurboQuant-MLX-2bit](https://huggingface.co/majentik/gemma-4-31b-it-turboquant-mlx-2bit) | mlx-lm | ~9.9 GB | Apple Silicon, smallest |
-| [TurboQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-31b-it-turboquant-mlx-4bit) | mlx-lm | ~19 GB | Apple Silicon balanced |
 | [TurboQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-31b-it-turboquant-mlx-8bit) | mlx-lm | ~37 GB | Apple Silicon reference |

 | Precision | Approximate Size | MLX Variant |
 |---|---|---|
 | FP16 (original) | ~62 GB | -- |
 | 4-bit quantized | ~17 GB | [RotorQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-31B-it-RotorQuant-MLX-4bit) |
 | **2-bit quantized** | **~9 GB** | **This model** |
 ## See Also
 - [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) -- Base model
 - [majentik/gemma-4-31B-it-RotorQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-31B-it-RotorQuant-MLX-4bit) -- MLX 4-bit variant
 - [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)
 - [MLX Framework](https://github.com/ml-explore/mlx)
 | [RotorQuant-GGUF-Q8_0](https://huggingface.co/majentik/gemma-4-31b-it-rotorquant-gguf-Q8_0) | llama.cpp | ~65 GB | Near-lossless reference |
 | **RotorQuant-MLX-2bit** | mlx-lm | ~9.9 GB | Apple Silicon, smallest |
 | [RotorQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-31b-it-rotorquant-mlx-4bit) | mlx-lm | ~19 GB | Apple Silicon balanced |
 | [TurboQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-31b-it-turboquant-mlx-8bit) | mlx-lm | ~37 GB | Apple Silicon reference |