Instructions to use thinkingmachines/Inkling-2-Small-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thinkingmachines/Inkling-2-Small-MXFP8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="thinkingmachines/Inkling-2-Small-MXFP8")
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("thinkingmachines/Inkling-2-Small-MXFP8")
model = AutoModelForMultimodalLM.from_pretrained("thinkingmachines/Inkling-2-Small-MXFP8", 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 thinkingmachines/Inkling-2-Small-MXFP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thinkingmachines/Inkling-2-Small-MXFP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thinkingmachines/Inkling-2-Small-MXFP8",
		"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/thinkingmachines/Inkling-2-Small-MXFP8

SGLang

How to use thinkingmachines/Inkling-2-Small-MXFP8 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 "thinkingmachines/Inkling-2-Small-MXFP8" \
    --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": "thinkingmachines/Inkling-2-Small-MXFP8",
		"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 "thinkingmachines/Inkling-2-Small-MXFP8" \
        --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": "thinkingmachines/Inkling-2-Small-MXFP8",
		"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 thinkingmachines/Inkling-2-Small-MXFP8 with Docker Model Runner:
```
docker model run hf.co/thinkingmachines/Inkling-2-Small-MXFP8
```

Add minimal quantization config to correct Hub parameter count

by burtenshaw HF Staff - opened about 3 hours ago

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

-1

burtenshaw

about 3 hours ago

Hugging Face CLI

Summary

Add the minimal top-level quantization metadata in config.json so the Hub reports approximately 594.05B parameters.

+  "quantization_config": {
+    "quant_method": "modelopt",
+    "quant_algo": "MXFP8"
+  }

The Hub's ModelOpt counting rules exclude the U8 *_weight_scale tensors (18,345,885,696 elements), while counting F8_E4M3 weights once.

Evidence

Ran computeNumOfParamsByDtypeSingleFile from @huggingface/hub@2.17.10 against the real safetensors headers of all 48 shards, pinned to 086a047e8b7ff2ac902dd52355328ddb66b16914. No weight payloads were downloaded.

Before: 612,396,527,616 parameters; exactly matches the current Hub count.
After: 594,050,641,920 parameters (594.05B).

Checked that the JSON is valid and this block is the only semantic change.

Merge Danger

Door: two-way — remove this block to revert.

Blast Radius: config

This validates Hub parameter counting only. Transformers/vLLM inference loading has not been validated. The current Transformers auto-quantizer registry does not register modelopt; maintainers should confirm the Inkling2 loading path accepts this metadata before merging.

Add minimal quantization config to correct Hub parameter countcaa74f77

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