Instructions to use ToPo-ToPo/gemma-4-31b-it-mlx-8bit 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-8bit 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-8bit") config = load_config("ToPo-ToPo/gemma-4-31b-it-mlx-8bit") # 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-8bit 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-8bit"
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-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ToPo-ToPo/gemma-4-31b-it-mlx-8bit 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-8bit"
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-8bit
Run Hermes
hermes
ToPo-ToPo/gemma-4-31b-it-mlx-8bit
MLX 8bit 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 8 --q-group-size 64 - Effective: 8.643 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-8bit")
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).
⚡ Faster generation with MTP (speculative decoding, lossless)
Recommended drafter: google/gemma-4-31b-it-assistant — Google's official MTP drafter for this
model. It loads directly in mlx-vlm (no conversion needed) and gives up to
~3x faster generation (≈1.4–1.5x measured on short prompts); output is
identical to non-MTP decoding.
# requires: pip install "mlx-vlm>=0.6.3"
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
model, processor = load("ToPo-ToPo/gemma-4-31b-it-mlx-8bit")
draft_model, _ = load("google/gemma-4-31b-it-assistant")
config = load_config("ToPo-ToPo/gemma-4-31b-it-mlx-8bit")
prompt = apply_chat_template(processor, config, "Hello!", num_images=0)
out = generate(model, processor, prompt,
draft_model=draft_model, draft_kind="mtp", max_tokens=256)
CLI (draft_kind auto-detected):
mlx_vlm.generate --model ToPo-ToPo/gemma-4-31b-it-mlx-8bit --draft-model google/gemma-4-31b-it-assistant
Notes
draft_kind="mtp"is required in the Python API (the CLI auto-detects it).- Use this model's own drafter above — drafters are size-specific and not interchangeable across Gemma 4 variants.
- Needs mlx-vlm >= 0.6.3. MTP is lossless — if output differs from non-MTP, your versions are mismatched.
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