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*This model was contributed to Hugging Face Transformers on 2026-07-15.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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# Inkling

[Inkling](https://huggingface.co/thinkingmachines/Inkling) is a general-purpose multimodal model from [Thinking Machines Lab](https://huggingface.co/thinkingmachines) that accepts text, image, and audio inputs and generates text. It is a 66-layer decoder-only transformer with a sparse mixture-of-experts (MoE) feed-forward backbone — each token is routed to 6 of 256 experts alongside 2 shared experts that are always active — for 975B total parameters with 41B active per token. Image and audio inputs are projected into the language model's embedding space and interleaved with text tokens, so a single checkpoint reasons jointly over all three modalities.

You can find the official checkpoints under the [Thinking Machines Lab](https://huggingface.co/thinkingmachines) organization.

The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline

model_id = "thinkingmachines/Inkling-NVFP4"
pipe = pipeline("image-text-to-text", model=model_id)

image_url = (
    "https://huggingface.co/datasets/merve/vl-test-suite/"
    "resolve/main/pills.jpg"
)
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": image_url,
            },
            {
                "type": "text",
                "text": "Do components in this supplement interact with each other?",
            },
        ],
    },
]
output = pipe(
    messages,
    max_new_tokens=2000,
    return_full_text=False,
    reasoning_effort="medium",
)
output[0]["generated_text"]
```

</hfoption>
<hfoption id="AutoModel">

```py
from transformers import AutoModelForMultimodalLM, AutoProcessor

model_id = "thinkingmachines/Inkling-NVFP4"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForMultimodalLM.from_pretrained(
    model_id,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You should only answer with a number."},
    {"role": "user", "content": "What is 17 * 23?"},
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    reasoning_effort="high",
).to(model.device)

output = model.generate(**inputs, max_new_tokens=2000)
generated_tokens = output[0][inputs["input_ids"].shape[1] :]
print(processor.decode(generated_tokens, skip_special_tokens=False))
```

</hfoption>
</hfoptions>

## Notes

- Text and image inference:

```py
from transformers import AutoModelForMultimodalLM, AutoProcessor

model_id = "thinkingmachines/Inkling"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForMultimodalLM.from_pretrained(
    model_id,
    device_map="auto",
)

image_url = (
    "https://huggingface.co/datasets/merve/vl-test-suite/"
    "resolve/main/pills.jpg"
)
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": image_url,
            },
            {
                "type": "text",
                "text": "Do any of the components in this supplement interact?",
            },
        ],
    },
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    reasoning_effort="medium",
    return_dict=True,
    return_tensors="pt",
).to(model.device)
input_len = inputs["input_ids"].shape[-1]

outputs = model.generate(**inputs, max_new_tokens=2000)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

processor.parse_response(response)
```

- Text with audio inference:

```py
from transformers import AutoModelForMultimodalLM, AutoProcessor

model_id = "thinkingmachines/Inkling"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForMultimodalLM.from_pretrained(
    model_id,
    device_map="auto",
)

audio_url = (
    "https://huggingface.co/datasets/merve/vl-test-suite/"
    "resolve/main/example_audio.mp3"
)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Transcribe the following speech to text."},
            {
                "type": "audio",
                "audio": audio_url,
            },
        ],
    },
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
).to(model.device)
input_len = inputs["input_ids"].shape[-1]

outputs = model.generate(**inputs, max_new_tokens=512)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

processor.parse_response(response)
```

- Serving with `transformers serve`:

```shell
transformers serve thinkingmachines/Inkling-NVFP4
```

```py
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="<random_string>")
completion = client.chat.completions.create(
    model="thinkingmachines/Inkling-NVFP4",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://huggingface.co/datasets/merve/vl-test-suite/resolve/main/pills.jpg"
                    },
                },
            ],
        }
    ],
)
print(completion.choices[0].message.content)
```

## InklingAudioConfig

[[autodoc]] InklingAudioConfig

## InklingConfig

[[autodoc]] InklingConfig

## InklingTextConfig

[[autodoc]] InklingTextConfig

## InklingVisionConfig

[[autodoc]] InklingVisionConfig

## InklingAudioModel

[[autodoc]] InklingAudioModel
    - forward

## InklingForCausalLM

[[autodoc]] InklingForCausalLM

## InklingForConditionalGeneration

[[autodoc]] InklingForConditionalGeneration

## InklingModel

[[autodoc]] InklingModel
    - forward

## InklingPreTrainedModel

[[autodoc]] InklingPreTrainedModel
    - forward

## InklingTextModel

[[autodoc]] InklingTextModel
    - forward

## InklingVisionModel

[[autodoc]] InklingVisionModel
    - forward

## InklingImageProcessor

[[autodoc]] InklingImageProcessor

## InklingFeatureExtractor

[[autodoc]] InklingFeatureExtractor

## InklingProcessor

[[autodoc]] InklingProcessor
