Instructions to use EmbeddedLLM/Inkling-Small-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EmbeddedLLM/Inkling-Small-MXFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="EmbeddedLLM/Inkling-Small-MXFP4")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("EmbeddedLLM/Inkling-Small-MXFP4")
model = AutoModelForMultimodalLM.from_pretrained("EmbeddedLLM/Inkling-Small-MXFP4", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EmbeddedLLM/Inkling-Small-MXFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EmbeddedLLM/Inkling-Small-MXFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EmbeddedLLM/Inkling-Small-MXFP4",
		"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/EmbeddedLLM/Inkling-Small-MXFP4

SGLang

How to use EmbeddedLLM/Inkling-Small-MXFP4 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 "EmbeddedLLM/Inkling-Small-MXFP4" \
    --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": "EmbeddedLLM/Inkling-Small-MXFP4",
		"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 "EmbeddedLLM/Inkling-Small-MXFP4" \
        --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": "EmbeddedLLM/Inkling-Small-MXFP4",
		"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 EmbeddedLLM/Inkling-Small-MXFP4 with Docker Model Runner:
```
docker model run hf.co/EmbeddedLLM/Inkling-Small-MXFP4
```

Inkling-Small-MXFP4

Model Overview

Model architecture: Thinking Machines Lab Inkling-Small
- Parameters: 276B total / 12B active
- Input: Text, image, audio
- Output: Text
Validated inference engine: vLLM
Model optimizer: AMD Quark (0.12.post1+rocm72.torch2.11)
- Quantized layers: MoE routed experts in transformer layers 3 through 41
- Weight quantization: OCP MXFP4, static, group size 32, E8M0 scales
- Activation quantization: OCP MXFP4, dynamic, group size 32, E8M0 scales

This checkpoint was built from thinkingmachines/Inkling-Small revision b2d4f225a02032c5d154bff748ab5a00c5ca26e4 by applying AMD Quark OCP MXFP4 quantization to the BF16 routed experts. Routed-expert weights are stored as packed MXFP4 weights with E8M0 scales. Dense layers 0 through 2, attention, shared experts, embeddings, norms, the audio and vision towers, MTP, and other non-routed components remain in their source formats.

Environment

The file-to-file conversion and validation targeted AMD gfx950 and used:

Container: docker.io/rocm/vllm-dev:nightly_main_20260714
Python: 3.12
ROCm/HIP: 7.2
PyTorch: 2.11.0+gitd0c8b1f
AMD Quark: 0.12.post1+rocm72.torch2.11
Expert chunk size: 8

The paired quality evaluation used the same GPU type and TP8 topology, with Transformers 5.14.1 and vLLM commit 846e2d01a0be00acf31f1a354059c7c302c93042 (0.23.1rc1.dev1212+g846e2d01a).

Evaluation

Benchmark	BF16 Reference	MXFP4	MXFP4 − BF16
BFCL exact calls	76.54% (1,034/1,351)	76.76% (1,037/1,351)	+0.22 pp
BFCL all-live macro	76.56%	71.01%	−5.55 pp
MMAU (official string match)	75.5% (755/1,000)	76.3% (763/1,000)	+0.80 pp
GPQA Diamond	89.19% (883/990)	87.98% (871/990)	−1.21 pp
AIME 2026	94.58% (908/960)	94.58% (908/960)	0.00 pp

Refer to the Inkling-Small model card for architecture, training, intended-use, safety, and acceptable-use details.

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Safetensors

Model size

148B params

Tensor type

F32

BF16

Model tree for EmbeddedLLM/Inkling-Small-MXFP4

Base model

thinkingmachines/Inkling-Small

Quantized

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