adapt model card to neat

README.md

@@ -36,33 +36,25 @@ The following performance metrics were measured with an image and a text prompt
 |---|---|---|---|---|
 | LFM2-VL-450M-a16w4 | A16W8/A16W4 | Modalix | 221.35 tokens/sec | 0.11 sec |
 
-
 ## Prerequisites
 
 To run this model, you need:
 
 1.  **SiMa.ai Modalix Device**
-2.  **SiMa.ai CLI**: [Installed](https://docs.sima.ai/pages/sima_cli/main.html#installation) on your Modalix device.
-3.  **Hugging Face CLI**: For downloading the model.
+2.  **SiMa.ai CLI**: Installed on your Modalix device.
+3.  **SiMa.ai Neat Runtime**: Install or update the Neat Library on Modalix. The LLiMa runtime is installed as part of the Neat runtime.
+4.  **Hugging Face CLI**: Optional, for downloading the model on a host before copying it to Modalix.
 
 ## Installation & Deployment
 
 Follow these steps to deploy the model to your Modalix device.
 
-### 1. Install LLiMa Demo Application
-> **Note:** This is a **one-time setup**. If you have already installed the LLiMa demo application (e.g. for another model), you can skip this step and continue with model download.
+### 1. Install or Update Neat Runtime
+> **Note:** This is a **one-time setup**. If the Neat Library is already installed on your Modalix device, you can skip this step and continue with model download.
 
-On your Modalix device, install the LLiMa demo application using the `sima-cli`:
+Follow the [SiMa.ai Neat getting started guide](https://developer.sima.ai/software/getting-started/) to install or update the Neat Library on your Modalix device.
 
-```bash
-# Create a directory for LLiMa
-cd /media/nvme
-mkdir llima
-cd llima
-# Install the LLiMa runtime code
-sima-cli install -v 2.1.0 tools/llima -t selection
-```
-> **Note:** To only download the LLiMa runtime code, select **Demo Web App** when prompted.
+The `llima` CLI is available on Modalix after the Neat runtime is installed. It manages precompiled GenAI models under `/media/nvme/llima/models` by default. Set `LLIMA_MODELS_PATH` to use a different model directory.
 
 ### 2. Download the Model
 
@@ -85,87 +77,51 @@ scp -r LFM2-VL-450M-a16w4 sima@<modalix-ip>:/media/nvme/llima/models/
 
 ```text
 /media/nvme/llima/
-├── run.sh
 └── models/
     └── LFM2-VL-450M-a16w4/   # The compiled model
 ```
 
 ## Usage
 
-### Run the Application
+### Validate with LLiMa CLI
 
-Navigate to the demo directory and start the application:
+Run the model directly on Modalix:
 
 ```bash
-cd /media/nvme/llima/
-./run.sh
+llima run LFM2-VL-450M-a16w4
 ```
 
-The script will detect the installed model(s) and prompt you to select one.
+For all runtime options, run:
 
-Once the application is running, open a browser and navigate to:
-```text
-https://<modalix-ip>:5000/
+```bash
+llima run -h
 ```
-*Replace \<modalix-ip\> with the IP address of your Modalix device.*
 
-### API Usage
+### GenAI Demo Application
 
-To use OpenAI-compatible API, run the model in API mode:
-```bash
-llima run LFM2-VL-450M-a16w4 --mode web
-```
+The GenAI demo application is separate from LLiMa installation. Use the [GenAI Multimodal Assistant](https://developer.sima.ai/examples/app/genai%2Fmultimodal-assistant) page to install and run the demo app. Once installed, the demo app can use precompiled models such as this one.
 
-You can interact with it using `curl` or Python.
+### API Usage
 
-**Example: Chat Completion**
+To serve this model with OpenAI- or Ollama-compatible APIs and send requests to it, use the GenAI server workflow in [Serve GenAI Models](https://developer.sima.ai/software/tutorials/serve-genai-models).
 
-```bash
-# Note: Replace <YOUR_BASE64_STRING_HERE> with an actual base64 encoded image string.
-curl -N -k -X POST "https://<modalix-ip>:9998/v1/chat/completions" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "sima-vlm",
-    "stream": true,
-    "messages": [
-      {
-        "role": "system",
-        "content": "You are a helpful assistant."
-      },
-      {
-        "role": "user",
-        "content": [
-          {
-            "type": "image_url",
-            "image_url": {
-              "url": "data:image/jpeg;base64,<YOUR_BASE64_STRING_HERE>"
-            }
-          },
-          {
-            "type": "text",
-            "text": "Describe the image in two sentences."
-          }
-        ]
-      }
-    ]
-  }'
-```
-*Replace \<modalix-ip\> with the IP address of your Modalix device.*
+For direct VLM calls without setting up a server, see [Run a VLM](https://developer.sima.ai/software/tutorials/run-a-vlm).
 
 ## Limitations
 
 - **Quantization**: This model is quantized (A16W4/A16W8) for optimal performance on embedded devices. While this maintains high accuracy, minor deviations from the full-precision model may occur.
 - **Fixed Resolution**: While the standard LFM2-VL architecture supports dynamic input resolutions, this version has been specifically optimized and fixed to **512x512** resolution at compile time to achieve maximum throughput and efficiency on the SiMa.ai MLA.
 
-
 ## Troubleshooting
 
-- **`sima-cli` not found**: Ensure that sima-cli is installed on your Modalix device.
+- **`sima-cli` not found**: Ensure that `sima-cli` is installed on your Modalix device.
+- **`llima` not found**: Install or update the Neat Library. See [Getting Started](https://developer.sima.ai/software/getting-started/).
 - **Model can't be run**: Verify the model directory is exactly inside `/media/nvme/llima/models/` and not nested (e.g., `/media/nvme/llima/models/LFM2-VL-450M-a16w4/LFM2-VL-450M-a16w4`).
 - **Permission Denied**: Ensure you have read/write permissions for the `/media/nvme` directory.
 
 ## Resources
 
-- [SiMa.ai Documentation](https://docs.sima.ai)
-- [SiMa.ai Hugging Face Organization](https://huggingface.co/simaai)
-- [Liquid AI Website](https://liquid.ai/)
+- [GenAI with LLiMa](https://developer.sima.ai/software/genai-llima/)
+- [Serve GenAI Models](https://developer.sima.ai/software/tutorials/serve-genai-models)
+- [Run a VLM](https://developer.sima.ai/software/tutorials/run-a-vlm)
+- [GenAI Multimodal Assistant](https://developer.sima.ai/examples/app/genai%2Fmultimodal-assistant)