Instructions to use ToPo-ToPo/gemma-4-31b-it-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ToPo-ToPo/gemma-4-31b-it-mlx-4bit 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("ToPo-ToPo/gemma-4-31b-it-mlx-4bit") config = load_config("ToPo-ToPo/gemma-4-31b-it-mlx-4bit") # 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
- Pi
How to use ToPo-ToPo/gemma-4-31b-it-mlx-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ToPo-ToPo/gemma-4-31b-it-mlx-4bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ToPo-ToPo/gemma-4-31b-it-mlx-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ToPo-ToPo/gemma-4-31b-it-mlx-4bit 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 "ToPo-ToPo/gemma-4-31b-it-mlx-4bit"
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 ToPo-ToPo/gemma-4-31b-it-mlx-4bit
Run Hermes
hermes
- OpenClaw new
How to use ToPo-ToPo/gemma-4-31b-it-mlx-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ToPo-ToPo/gemma-4-31b-it-mlx-4bit"
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 "ToPo-ToPo/gemma-4-31b-it-mlx-4bit" \ --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"
ToPo-ToPo/gemma-4-31b-it-mlx-4bit
MLX 4bit conversion of google/gemma-4-31b-it for Apple Silicon (mlx-vlm).
Provenance (self-converted from official weights)
- Source:
google/gemma-4-31b-it(license: gemma) - Tool:
mlx-vlm 0.6.3—mlx_vlm.convert --hf-path google/gemma-4-31b-it --mlx-path . -q --q-bits 4 --q-group-size 64 - Effective: 4.713 bits/weight
- Validation: reproduced geometrically exact CAD output in an agentic CAD+FEM pipeline (volumes match the reference mlx-community conversion).
Usage
from mlx_vlm import load, generate
model, processor = load("ToPo-ToPo/gemma-4-31b-it-mlx-4bit")
License
This is a derivative of Google Gemma. Use is governed by the Gemma Terms of Use and the Gemma Prohibited Use Policy. Weights were converted/quantized to MLX format (modification notice per the Gemma Terms).
⚡ MTP drafter (speculative decoding)
Use google/gemma-4-31b-it-assistant — Google's official MTP drafter for
this model. It loads directly in mlx-vlm (>= 0.6.3), needs no conversion, and speculative
decoding is lossless. Drafters are size-specific and not interchangeable across Gemma 4
variants.
🔧 Patched chat template
chat_template.jinja differs from Google's Gemma 4 Canonical Chat Template
(2026-07-09) by one intentional change; everything else is untouched.
The canonical template suppresses the thinking channel at the start of a normal model
turn, but emits nothing after a tool response when enable_thinking is false. The model
may then open a thinking channel on its own, and a quantized model sometimes writes the
literal word thought into the answer. This patch gives the tool_response branch the
same suppression:
{%- elif ns.prev_message_type == 'tool_response' -%}
{%- if enable_thinking -%}
{{- '<|channel>thought\n' -}}
{%- else -%}
{{- '<|channel>thought\n<channel|>' -}}
{%- endif -%}
{%- endif -%}
Only that case changes — the other prompt paths render byte-identical to the canonical
template. To get stock behaviour, replace chat_template.jinja with the one from the
base model repo; the weights are unaffected.
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