> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# Kimi-VL

[Kimi-VL](https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct) and Kimi-K25-VL are vision language models from Moonshot AI. Kimi-VL-A3B uses a MoE language backbone (3B active parameters) with a vision encoder, supporting image understanding and multimodal reasoning.

|                  |                                                 |
| ---------------- | ----------------------------------------------- |
| **Task**         | Image-Text-to-Text                              |
| **Architecture** | `KimiVLForConditionalGeneration`                |
| **Parameters**   | \~3B active (MoE)                               |
| **HF Org**       | [moonshotai](https://huggingface.co/moonshotai) |

## Available Models

* **Kimi-VL-A3B-Instruct**
* **Kimi-K25-VL**

## Architecture

* `KimiVLForConditionalGeneration`

## Example HF Models

| Model                | HF ID                                                                                       |
| -------------------- | ------------------------------------------------------------------------------------------- |
| Kimi-VL-A3B-Instruct | [`moonshotai/Kimi-VL-A3B-Instruct`](https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct) |

## Example Recipes

| Recipe                                                                                                                      | Dataset    | Description                 |
| --------------------------------------------------------------------------------------------------------------------------- | ---------- | --------------------------- |
| [kimi2vl\_cordv2.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/kimi/kimi2vl_cordv2.yaml)   | cord-v2    | SFT — Kimi-VL on CORD-v2    |
| [kimi25vl\_medpix.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/kimi/kimi25vl_medpix.yaml) | MedPix-VQA | SFT — Kimi-K25-VL on MedPix |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

**3. Run the recipe** from inside the repo:

```bash
automodel --nproc-per-node=8 examples/vlm_finetune/kimi/kimi2vl_cordv2.yaml
```

**1. Pull the container** and mount a checkpoint directory:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.06.00
```

**2.** Navigate to the AutoModel directory (where the recipes are):

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

```bash
automodel --nproc-per-node=8 examples/vlm_finetune/kimi/kimi2vl_cordv2.yaml
```

See the [Installation Guide](/get-started/installation) and [VLM Fine-Tuning Guide](/recipes-e2e-examples/gemma-3-3n).

## Fine-Tuning

See the [VLM Fine-Tuning Guide](/recipes-e2e-examples/gemma-3-3n).

## Hugging Face Model Cards

* [moonshotai/Kimi-VL-A3B-Instruct](https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct)