Instructions to use luokelabs/gemma-4-26b-a4b-it-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use luokelabs/gemma-4-26b-a4b-it-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("luokelabs/gemma-4-26b-a4b-it-8bit") config = load_config("luokelabs/gemma-4-26b-a4b-it-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 luokelabs/gemma-4-26b-a4b-it-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 "luokelabs/gemma-4-26b-a4b-it-8bit"
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": "luokelabs/gemma-4-26b-a4b-it-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use luokelabs/gemma-4-26b-a4b-it-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 "luokelabs/gemma-4-26b-a4b-it-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 luokelabs/gemma-4-26b-a4b-it-8bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use luokelabs/gemma-4-26b-a4b-it-8bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "luokelabs/gemma-4-26b-a4b-it-8bit"
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 "luokelabs/gemma-4-26b-a4b-it-8bit" \ --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-4-26b-a4b-it-8bit(镜像)
这是 mlx-community/gemma-4-26b-a4b-it-8bit 的镜像,供螺壳(luoke)的本地应用在国内下载:螺壳主控按发布清单取这里的文件、逐文件核对哈希后用作 64 GB 以上内存档的底座模型。权重、config、tokenizer 等文件与 mlx-community 的原版逐字节相同(8 bit 仿射量化,group size 64),只补了这份说明和 LICENSE。上游模型 google/gemma-4-26b-a4b-it(Google)为 Apache License 2.0(见 Gemma 4 许可页);Google 另有禁止用途政策和使用条款,使用前请阅读。量化由 mlx-community 用 mlx-vlm 0.4.3 完成。
以下为 mlx-community 原模型卡:
mlx-community/gemma-4-26b-a4b-it-8bit
This model was converted to MLX format from google/gemma-4-26b-a4b-it
using mlx-vlm version 0.4.3.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model mlx-community/gemma-4-26b-a4b-it-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
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