Image-Text-to-Text
MLX
Safetensors
gemma4_unified
quantized
mixed-precision
4bit
8bit
optiq
apple-silicon
vision-language
gemma4
coding
code
reasoning
thinking
conversational
4-bit precision
Instructions to use mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-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("mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-4bit") config = load_config("mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-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 mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-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 "mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-4bit"
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": "mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-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 "mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-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 mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-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 "mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-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 "mlx-community/gemma-4-12B-coder-fable5-composer2.5-v1-OptiQ-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"
Upload tokenizer_config.json with huggingface_hub
Browse files- tokenizer_config.json +97 -0
tokenizer_config.json
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{
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"audio_token": "<|audio|>",
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"backend": "tokenizers",
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"boa_token": "<|audio>",
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"boi_token": "<|image>",
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"bos_token": "<bos>",
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"eoa_token": "<audio|>",
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"eoc_token": "<channel|>",
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"eoi_token": "<image|>",
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"eos_token": "<eos>",
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"eot_token": "<turn|>",
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"escape_token": "<|\"|>",
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"etc_token": "<tool_call|>",
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"etd_token": "<tool|>",
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"etr_token": "<tool_response|>",
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"extra_special_tokens": [
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"<|video|>"
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],
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"image_token": "<|image|>",
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"is_local": true,
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"local_files_only": false,
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"mask_token": "<mask>",
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"model_max_length": 1000000000000000019884624838656,
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"model_specific_special_tokens": {
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"audio_token": "<|audio|>",
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"boa_token": "<|audio>",
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"boi_token": "<|image>",
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"eoa_token": "<audio|>",
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"eoc_token": "<channel|>",
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"eoi_token": "<image|>",
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"eot_token": "<turn|>",
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"escape_token": "<|\"|>",
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"etc_token": "<tool_call|>",
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"etd_token": "<tool|>",
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"etr_token": "<tool_response|>",
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"image_token": "<|image|>",
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"soc_token": "<|channel>",
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"sot_token": "<|turn>",
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"stc_token": "<|tool_call>",
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"std_token": "<|tool>",
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"str_token": "<|tool_response>",
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"think_token": "<|think|>"
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},
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"pad_token": "<pad>",
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"padding_side": "left",
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"processor_class": "Gemma4UnifiedProcessor",
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"response_schema": {
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"properties": {
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"content": {
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"type": "string"
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},
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"role": {
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"const": "assistant"
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},
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"thinking": {
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"type": "string"
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},
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"tool_calls": {
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"items": {
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"properties": {
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"function": {
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"properties": {
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"arguments": {
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"additionalProperties": {},
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"type": "object",
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"x-parser": "gemma4-tool-call"
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},
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"name": {
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"type": "string"
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}
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},
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"type": "object",
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"x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})"
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},
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"type": {
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"const": "function"
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}
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},
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"type": "object"
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},
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"type": "array",
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"x-regex-iterator": "<\\|tool_call>(.*?)<tool_call\\|>"
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}
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},
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"type": "object",
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"x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?P<content>(?:(?!\\<turn\\|\\>)(?!\\<\\|tool_response\\>).)+)?(?:\\<turn\\|\\>|\\<\\|tool_response\\>)?"
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},
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"soc_token": "<|channel>",
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"sot_token": "<|turn>",
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"stc_token": "<|tool_call>",
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"std_token": "<|tool>",
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"str_token": "<|tool_response>",
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"think_token": "<|think|>",
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"tokenizer_class": "GemmaTokenizer",
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"tool_parser_type": "gemma4",
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"unk_token": "<unk>"
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}
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