Image-Text-to-Text
MLX
Safetensors
gemma4
lemma
4bit
apple-silicon
multimodal
on-device
conversational
Instructions to use lthn/lemer-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use lthn/lemer-mlx 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("lthn/lemer-mlx") config = load_config("lthn/lemer-mlx") # 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 lthn/lemer-mlx with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "lthn/lemer-mlx"
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": "lthn/lemer-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use lthn/lemer-mlx 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 "lthn/lemer-mlx"
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 lthn/lemer-mlx
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use lthn/lemer-mlx with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "lthn/lemer-mlx"
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 "lthn/lemer-mlx" \ --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"
Snider Virgil commited on
Commit ·
0e862eb
1
Parent(s): 662a530
docs: correct base_model lineage for HF model tree
Browse filesHF uses base_model + base_model_relation frontmatter to rank models in
search results and render the model tree widget. The Lemma family's
true lineage is:
google/gemma-4-*-it
└── LetheanNetwork/<m> (finetune — our namespace fork)
└── lthn/<m> (finetune — LEK merged into weights)
└── lthn/<m>-mlx (quantized — mlx 4/8bit/bf16)
Previously this repo had base_model_relation set to quantized, which
was wrong — LEK merging is a finetune, not a quant. Fixing so the
model tree widget ranks the family correctly.
Co-Authored-By: Virgil <virgil@lethean.io>
README.md
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pipeline_tag: image-text-to-text
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library_name: mlx
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base_model:
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base_model_relation: quantized
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tags:
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---
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<!--
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This content is subject to the European Union Public Licence (EUPL-1.2).
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pipeline_tag: image-text-to-text
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library_name: mlx
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base_model:
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base_model_relation: quantized
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tags:
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license_link: https://ai.google.dev/gemma/docs/gemma_4_license
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---
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<!--
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This content is subject to the European Union Public Licence (EUPL-1.2).
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