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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="lightseekorg/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("lightseekorg/Inkling-Small-MXFP4")
model = AutoModelForMultimodalLM.from_pretrained("lightseekorg/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 lightseekorg/Inkling-Small-MXFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lightseekorg/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": "lightseekorg/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/lightseekorg/Inkling-Small-MXFP4

SGLang

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

Inkling-Small-MXFP4 / README.md

lightseek

Upload MXFP4 model weights

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	---
	license: apache-2.0
	base_model:
	- thinkingmachines/Inkling-Small
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- amd-quark
	- mxfp4
	- rocm
	- tokenspeed
	---

	# Inkling-Small-MXFP4

	## Model Overview

	- Base model: [thinkingmachines/Inkling-Small](https://huggingface.co/thinkingmachines/Inkling-Small)
	- Architecture: 42-layer multimodal sparse Mixture-of-Experts transformer
	- Parameters: 276B total, 12B active
	- Input: Text, image, and audio
	- Output: Text
	- Inference engine: [TokenSpeed](https://github.com/lightseekorg/tokenspeed)
	- Model optimizer: [AMD Quark](https://github.com/amd/quark) (`0.12.post1+rocm72.torch2.11`)
	- Quantized layers: MoE routed experts in transformer layers 3-41
	- Weight quantization: OCP MXFP4, static, group size 32
	- Activation quantization: OCP MXFP4, dynamic, group size 32

	This checkpoint was produced by applying AMD Quark MXFP4 file-to-file quantization to the BF16 Inkling-Small checkpoint. Attention layers, shared experts, router weights, embeddings, normalization layers, multimodal components, MTP weights, and transformer layers 0-2 remain in BF16.

	## Environment

	The quantization was performed on an AMD gfx950 system with the following software:

	- GPU: AMD MI350/MI355
	- Target graphics version: gfx950
	- ROCm: 7.2.1
	- Python: 3.12.3
	- PyTorch: 2.13.0+rocm7.1
	- AMD Quark: 0.12.post1+rocm72.torch2.11
	- Safetensors: 0.8.0

	Create and activate a dedicated Quark environment:

	```bash
	python3 -m venv ~/.venv-quark
	source ~/.venv-quark/bin/activate
	```

	Install the required packages:

	```bash
	python -m pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm7.1
	python -m pip install amd-quark --extra-index-url https://pypi.amd.com/quark/rocm72/simple
	python -m pip install safetensors transformers accelerate tqdm
	```

	## Model Quantization

	The included `quantize_quark.py` uses Quark's file-to-file flow to process safetensor shards without loading the full BF16 checkpoint into GPU memory. It automatically applies the Inkling-Small exclusion policy and quantizes only routed expert weights in layers 3-41.

	```bash
	python quantize_quark.py \
	--model_dir /path/to/Inkling-Small \
	--output_dir /path/to/Inkling-Small-MXFP4 \
	--quant_scheme mxfp4 \
	--file2file_quantization
	```

	## Deployment

	This model can be served with [TokenSpeed](https://github.com/lightseekorg/tokenspeed):

	```bash
	tokenspeed serve \
	--model lightseekorg/Inkling-Small-MXFP4 \
	--attn-tp-size 1 \
	--moe-tp-size 1 \
	--max-model-len 81920 \
	--max-num-seqs 16 \
	--max-prefill-tokens 8192 \
	--chunked-prefill-size 8192 \
	--gpu-memory-utilization 0.95 \
	--disable-cuda-graph-padding \
	--trust-remote-code \
	--dtype bfloat16 \
	--disable-kvstore \
	--kvstore-ratio 0 \
	--block-size 128 \
	--speculative-algorithm MTP \
	--speculative-num-steps 3 \
	--speculative-eagle-topk 1 \
	--speculative-num-draft-tokens 4 \
	--host 127.0.0.1 \
	--port 22015
	```