Instructions to use tiny-random/inkling-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/inkling-2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="tiny-random/inkling-2", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("tiny-random/inkling-2", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("tiny-random/inkling-2", trust_remote_code=True, device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/inkling-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/inkling-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/inkling-2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/inkling-2

SGLang

How to use tiny-random/inkling-2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tiny-random/inkling-2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/inkling-2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tiny-random/inkling-2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/inkling-2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use tiny-random/inkling-2 with Docker Model Runner:
```
docker model run hf.co/tiny-random/inkling-2
```

inkling-2 / README.md

yujiepan

Restore independent Inkling 2 embedding and output head

4c88f31 verified about 6 hours ago

preview code

Raw History Blame Contribute Delete

3.55 kB

---
library_name: transformers
pipeline_tag: image-text-to-text
base_model:
- thinkingmachines/Inkling-2-Small
---
This is a randomly initialized tiny model based on [thinkingmachines/Inkling-2-Small](https://huggingface.co/thinkingmachines/Inkling-2-Small) for testing and debugging.

| Feature | Source | Tiny |
|---|---|---|
| Checkpoint tensor elements (including packed weights/scales) | 8.69B BF16, 565.33K F32, 128.42B F8_E4M3, 247.67B U8 | 16.40M BF16, 3.21K F32, 1.88M F8_E4M3, 940.03K U8 |
| Safetensors weight files (including MTP) | 393.48 GB (25 files) | 35.68 MB (2 files) |
| Decoder layers / attention residual block | 48 / 6 | 12 / 6 |
| Attention cycle | 5 SWA + 1 global | Same; two complete cycles |
| Routed experts / experts per token | 384 / 8 | 96 / 8 |
| Expert precision | 40 NVFP4 + 8 MXFP8 layers | 6 NVFP4 + 6 MXFP8 layers |
| Vocabulary / tied embeddings | 201,024 / false | 201,024 / false |
| Hidden / expert width | 4,608 / 2,304 | 32 / 32 |
| Q heads / KV heads / head dimension | 48 / 8 / 128 | 24 / 4 / 32 |
| Vision width / layers | 1,024 / 23 | 32 / 4 |
| MTP layers / block size / Markov rank | 5 / 8 / 256 | 5 / 8 / 8 |
| MTP feature taps | 5,11,17,23,29,35,41,48 | 5,12 (final norm sentinel) |

| File path | Size |
|------|------|
| model.safetensors | 31.7MB |
| mtp.safetensors | 4.0MB |

### Example usage

#### Transformers (unverified)

Install [Transformers with Inkling 2 support](https://github.com/huggingface/transformers/tree/c748111b3bb4d97df452f0fe60e63c480beb6fe6). The bundled loader expands packed expert weights to BF16.

```python
def run_transformers(model_id: str):
    import torch
    from PIL import Image
    from transformers import AutoModelForImageTextToText, AutoProcessor
    processor = AutoProcessor.from_pretrained(model_id)
    model = AutoModelForImageTextToText.from_pretrained(
        model_id, trust_remote_code=True, dtype=torch.bfloat16, device_map="cuda"
    )
    image = Image.new("RGB", (80, 80), (80, 120, 160))
    inputs = processor(text=f"{processor.image_token}Describe this image.", images=image, return_tensors="pt").to(
        model.device
    )
    with torch.inference_mode():
        output = model.generate(**inputs, max_new_tokens=10, min_new_tokens=10, do_sample=False)
    print(processor.batch_decode(output[:, inputs.input_ids.shape[1] :], skip_special_tokens=True))
if __name__ == "__main__":
    run_transformers(model_id="tiny-random/inkling-2")
```

#### SGLang serving (unverified)

Use the pinned SGLang version and the bundled small-matrix kernel plugin.

```bash
SGLANG_BUILD_RUST_EXTS=none pip install 'git+https://github.com/sgl-project/sglang.git@f726172d760dea737c7f1e797c9f32efe14d1685#subdirectory=python'
hf download tiny-random/inkling-2 --local-dir inkling-2-tiny
pip install --no-deps ./inkling-2-tiny
sglang serve --model-path ./inkling-2-tiny --tp 4 --ep 4 \
  --dtype bfloat16 --attention-backend fa4 --enable-multimodal \
  --context-length 2048 --mem-fraction-static 0.2 \
  --disable-custom-all-reduce \
  --cuda-graph-config '{"decode": {"bs": [1, 2, 4]}, "prefill": {"bs": [128, 2048]}}' \
  --reasoning-parser inkling --tool-call-parser inkling2
```

### Creating this repo

- Code: [.meta/create_model.py](.meta/create_model.py)
- Model structure: [.meta/model.log](.meta/model.log)
- Environment: [.meta/env.json](.meta/env.json)

### Change log

- 2026-11-14: Updated tiny Inkling 2 with 96 mixed NVFP4/MXFP8 experts, independent token embeddings/output head, and BF16 MTP.

*This repo is generated by GPT-6.1-Sol*

	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model:
	- thinkingmachines/Inkling-2-Small
	---

	This is a randomly initialized tiny model based on [thinkingmachines/Inkling-2-Small](https://huggingface.co/thinkingmachines/Inkling-2-Small) for testing and debugging.

	\| Feature \| Source \| Tiny \|
	\|---\|---\|---\|
	\| Checkpoint tensor elements (including packed weights/scales) \| 8.69B BF16, 565.33K F32, 128.42B F8_E4M3, 247.67B U8 \| 16.40M BF16, 3.21K F32, 1.88M F8_E4M3, 940.03K U8 \|
	\| Safetensors weight files (including MTP) \| 393.48 GB (25 files) \| 35.68 MB (2 files) \|
	\| Decoder layers / attention residual block \| 48 / 6 \| 12 / 6 \|
	\| Attention cycle \| 5 SWA + 1 global \| Same; two complete cycles \|
	\| Routed experts / experts per token \| 384 / 8 \| 96 / 8 \|
	\| Expert precision \| 40 NVFP4 + 8 MXFP8 layers \| 6 NVFP4 + 6 MXFP8 layers \|
	\| Vocabulary / tied embeddings \| 201,024 / false \| 201,024 / false \|
	\| Hidden / expert width \| 4,608 / 2,304 \| 32 / 32 \|
	\| Q heads / KV heads / head dimension \| 48 / 8 / 128 \| 24 / 4 / 32 \|
	\| Vision width / layers \| 1,024 / 23 \| 32 / 4 \|
	\| MTP layers / block size / Markov rank \| 5 / 8 / 256 \| 5 / 8 / 8 \|
	\| MTP feature taps \| 5,11,17,23,29,35,41,48 \| 5,12 (final norm sentinel) \|

	\| File path \| Size \|
	\|------\|------\|
	\| model.safetensors \| 31.7MB \|
	\| mtp.safetensors \| 4.0MB \|

	### Example usage

	#### Transformers (unverified)

	Install [Transformers with Inkling 2 support](https://github.com/huggingface/transformers/tree/c748111b3bb4d97df452f0fe60e63c480beb6fe6). The bundled loader expands packed expert weights to BF16.

	```python
	def run_transformers(model_id: str):
	import torch
	from PIL import Image
	from transformers import AutoModelForImageTextToText, AutoProcessor

	processor = AutoProcessor.from_pretrained(model_id)
	model = AutoModelForImageTextToText.from_pretrained(
	model_id, trust_remote_code=True, dtype=torch.bfloat16, device_map="cuda"
	)
	image = Image.new("RGB", (80, 80), (80, 120, 160))
	inputs = processor(text=f"{processor.image_token}Describe this image.", images=image, return_tensors="pt").to(
	model.device
	)
	with torch.inference_mode():
	output = model.generate(**inputs, max_new_tokens=10, min_new_tokens=10, do_sample=False)
	print(processor.batch_decode(output[:, inputs.input_ids.shape[1] :], skip_special_tokens=True))


	if __name__ == "__main__":
	run_transformers(model_id="tiny-random/inkling-2")
	```

	#### SGLang serving (unverified)

	Use the pinned SGLang version and the bundled small-matrix kernel plugin.

	```bash
	SGLANG_BUILD_RUST_EXTS=none pip install 'git+https://github.com/sgl-project/sglang.git@f726172d760dea737c7f1e797c9f32efe14d1685#subdirectory=python'
	hf download tiny-random/inkling-2 --local-dir inkling-2-tiny
	pip install --no-deps ./inkling-2-tiny
	sglang serve --model-path ./inkling-2-tiny --tp 4 --ep 4 \
	--dtype bfloat16 --attention-backend fa4 --enable-multimodal \
	--context-length 2048 --mem-fraction-static 0.2 \
	--disable-custom-all-reduce \
	--cuda-graph-config '{"decode": {"bs": [1, 2, 4]}, "prefill": {"bs": [128, 2048]}}' \
	--reasoning-parser inkling --tool-call-parser inkling2
	```

	### Creating this repo

	- Code: [.meta/create_model.py](.meta/create_model.py)
	- Model structure: [.meta/model.log](.meta/model.log)
	- Environment: [.meta/env.json](.meta/env.json)

	### Change log

	- 2026-11-14: Updated tiny Inkling 2 with 96 mixed NVFP4/MXFP8 experts, independent token embeddings/output head, and BF16 MTP.

	This repo is generated by GPT-6.1-Sol