Instructions to use OsaurusAI/gemma-4-31B-it-qat-JANG_4M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsaurusAI/gemma-4-31B-it-qat-JANG_4M 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("OsaurusAI/gemma-4-31B-it-qat-JANG_4M")
config = load_config("OsaurusAI/gemma-4-31B-it-qat-JANG_4M")

# 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 OsaurusAI/gemma-4-31B-it-qat-JANG_4M with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/gemma-4-31B-it-qat-JANG_4M"

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": "OsaurusAI/gemma-4-31B-it-qat-JANG_4M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use OsaurusAI/gemma-4-31B-it-qat-JANG_4M 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 "OsaurusAI/gemma-4-31B-it-qat-JANG_4M"

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 OsaurusAI/gemma-4-31B-it-qat-JANG_4M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OsaurusAI/gemma-4-31B-it-qat-JANG_4M with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/gemma-4-31B-it-qat-JANG_4M"

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 "OsaurusAI/gemma-4-31B-it-qat-JANG_4M" \
  --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"

gemma-4-31B-it-qat-JANG_4M / README.md

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	---
	language:
	- en
	library_name: mlx
	license: gemma
	pipeline_tag: image-text-to-text
	base_model: google/gemma-4-31B-it-qat-q4_0-unquantized
	tags:
	- mlx
	- jang
	- jang-4m
	- qat
	- quantized
	- apple-silicon
	- gemma4
	- vision
	---

	<p align="center"><a href="https://osaurus.ai"><img src="./osaurus-x-banner.png" alt="Osaurus AI"></a></p>

	# OsaurusAI/gemma-4-31B-it-qat-JANG_4M

	JANG_4M MLX affine bundle converted from [google/gemma-4-31B-it-qat-q4_0-unquantized](https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-unquantized).

	This bundle keeps Gemma 4 bookends and media-sensitive components coherent: token embeddings/output projection, norms, media towers/embedders, and Gemma 4 per-layer embedding/gate/projector tensors are fp16 passthrough; self-attention and MoE router projections are 8-bit affine; decoder MLP/expert bulk is 4-bit affine.

	## Bundle

	\| Field \| Value \|
	\|---\|---\|
	\| Source \| `google/gemma-4-31B-it-qat-q4_0-unquantized` \|
	\| Architecture \| `gemma4` / `Gemma4ForConditionalGeneration` \|
	\| Text layers \| 60 \|
	\| Hidden size \| 5376 \|
	\| Weight format \| `jang_affine` \|
	\| Top-level quantization \| bits=8, group_size=32, mode=affine \|
	\| Tier bits \| attention=8, router=8, mlp=4, embed=16, per_layer_media=16 \|
	\| Quantized modules \| 410 affine bases with `.scales` and `.biases` sidecars \|
	\| Shards \| 25 safetensors shards \|
	\| Capabilities \| `vision` \|

	## Modalities

	\| Modality \| Status \|
	\|---\|---\|
	\| Text \| supported \|
	\| Vision \| source config present and preserved \|
	\| Audio \| `audio_config` is empty/not active for this variant; this card does not claim audio support. \|
	\| Video \| No `video_config` is present, so this card does not claim a verified video runtime path. \|

	Runtime support depends on a Gemma 4 compatible MLX/vMLX loader that understands `config.json` `quantization` overrides, `jang_config.json`, and Gemma 4 processor/chat-template files.

	## Runtime Metadata

	- `config.json` has source-derived `has_vision`, `has_audio`, `has_video`, `modalities`, and `capabilities`.
	- `tokenizer_config.json` includes `bos_token_id`, `eos_token_id`, `pad_token_id`, and the patched Gemma 4 chat template.
	- `processor_config.json` is preserved for Gemma 4 multimodal processing.
	- MTP/speculative drafter weights are not present in the source checkpoint; metadata is `mtp: none` / `mtp_policy: none`.