๐๏ธ LFM2.5-VL
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How to use LiquidAI/LFM2.5-VL-450M-MLX-5bit 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/LFM2.5-VL-450M-MLX-5bit")
config = load_config("LiquidAI/LFM2.5-VL-450M-MLX-5bit")
# 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)How to use LiquidAI/LFM2.5-VL-450M-MLX-5bit with Pi:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "LiquidAI/LFM2.5-VL-450M-MLX-5bit"
# 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": "LiquidAI/LFM2.5-VL-450M-MLX-5bit"
}
]
}
}
}# Start Pi in your project directory: pi
How to use LiquidAI/LFM2.5-VL-450M-MLX-5bit with Hermes Agent:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "LiquidAI/LFM2.5-VL-450M-MLX-5bit"
# 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/LFM2.5-VL-450M-MLX-5bit
hermes
Try LFM โข Documentation โข LEAP โข Blog
MLX export of LFM2.5-VL-450M for Apple Silicon inference.
LFM2.5-VL-450M is a vision-language model built on the LFM2.5-350M backbone with a SigLIP2 NaFlex vision encoder (86M). It supports OCR, document comprehension, multilingual vision understanding, bounding box prediction, and function calling.
| Property | Value |
|---|---|
| Parameters | 450M |
| Precision | 5-bit |
| Group Size | 64 |
| Size | 0.39 GB |
| Context Length | 32K |
| Vision Encoder | SigLIP2 NaFlex (86M) |
| Native Resolution | up to 512x512 |
uv pip install 'mlx-vlm==0.3.9'
from mlx_vlm import load, generate
from mlx_vlm.utils import load_image
model, processor = load("LiquidAI/LFM2.5-VL-450M-MLX-5bit")
image = load_image("photo.jpg")
# Apply chat template (required for LFM2.5-VL)
messages = [{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": "What do you see in this image?"},
]}]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
result = generate(
model,
processor,
prompt,
[image],
temp=0.1,
min_p=0.15,
repetition_penalty=1.05,
verbose=True,
)
print(result.text)
| Parameter | Value |
|---|---|
| temperature | 0.1 |
| min_p | 0.15 |
| repetition_penalty | 1.05 |
This model is released under the LFM 1.0 License.
5-bit
Base model
LiquidAI/LFM2.5-350M-Base