Image-Text-to-Text
MLX
Safetensors
lfm3_vl
liquid
lfm3
edge
decision
classification
calibration
system-one
multimodal
vision
conversational
custom_code
Instructions to use LiquidAI/d1-2B-MLX-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use LiquidAI/d1-2B-MLX-fp16 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("LiquidAI/d1-2B-MLX-fp16") config = load_config("LiquidAI/d1-2B-MLX-fp16") # 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 LiquidAI/d1-2B-MLX-fp16 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "LiquidAI/d1-2B-MLX-fp16"
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": "LiquidAI/d1-2B-MLX-fp16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use LiquidAI/d1-2B-MLX-fp16 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 "LiquidAI/d1-2B-MLX-fp16"
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 LiquidAI/d1-2B-MLX-fp16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use LiquidAI/d1-2B-MLX-fp16 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "LiquidAI/d1-2B-MLX-fp16"
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 "LiquidAI/d1-2B-MLX-fp16" \ --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"
Download tokenizer.json from LiquidAI/d1-2B-MLX-fp16: direct link, hf CLI and curl.
- Browser
- Download file 17.9 MB
-
https://huggingface.co/LiquidAI/d1-2B-MLX-fp16/resolve/main/tokenizer.json
- Command line
-
hf download hf://LiquidAI/d1-2B-MLX-fp16/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/LiquidAI/d1-2B-MLX-fp16/resolve/main/tokenizer.json
17.9 MB
- Xet hash:
- 0c6ae2ae860eab63090922b6595184b27e5518f7caaa263b8c10fd8c3bb367e3
- Size of remote file:
- 17.9 MB
- SHA256:
- a39eed58008cbcbc09e5b629b81338a654269c64cd13348aaa6090f4db75c7e7
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