Instructions to use RedHatAI/Inkling-NVFP4-FP8-BLOCK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Inkling-NVFP4-FP8-BLOCK with Transformers:

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

pipe = pipeline("image-text-to-text", model="RedHatAI/Inkling-NVFP4-FP8-BLOCK")
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("RedHatAI/Inkling-NVFP4-FP8-BLOCK")
model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/Inkling-NVFP4-FP8-BLOCK", 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 RedHatAI/Inkling-NVFP4-FP8-BLOCK with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Inkling-NVFP4-FP8-BLOCK"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Inkling-NVFP4-FP8-BLOCK",
		"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/RedHatAI/Inkling-NVFP4-FP8-BLOCK

SGLang

How to use RedHatAI/Inkling-NVFP4-FP8-BLOCK 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 "RedHatAI/Inkling-NVFP4-FP8-BLOCK" \
    --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": "RedHatAI/Inkling-NVFP4-FP8-BLOCK",
		"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 "RedHatAI/Inkling-NVFP4-FP8-BLOCK" \
        --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": "RedHatAI/Inkling-NVFP4-FP8-BLOCK",
		"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 RedHatAI/Inkling-NVFP4-FP8-BLOCK with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Inkling-NVFP4-FP8-BLOCK
```

RedHatAI/Inkling-NVFP4-FP8-BLOCK

This is a quantized version of thinkingmachines/Inkling with MoE layers quantized to NVFP4 and attention layers quantized to FP8 Block.

Usage

This model is intended for deployment with vLLM. You can serve the model using 4xB200s:

vllm serve RedHatAI/Inkling-NVFP4-FP8-BLOCK \
  --tokenizer-mode inkling \
  --tensor-parallel-size 4 \ 
  --enable-auto-tool-choice \
  --tool-call-parser inkling \
  --reasoning-parser inkling \
  --enable-expert-parallel \
  --max-model-len 100000 \
  --max-num-seqs 32 \
  --enable-chunked-prefill \
  --trust-remote-code \

Evaluation

Evaluations were performed with inspectai:

inspect eval hf/Idavidrein/gpqa/diamond \
  --model RedHatAI/Inkling-NVFP4-FP8-BLOCK \
  --reasoning-effort high \
  --model-base-url http://localhost:8000/v1 \

Benchmark	`thinkingmachines/Inkling`	`RedHatAI/Inkling-NVFP4-FP8-BLOCK`
GPQA Diamond	87.2	84.5

Note: A bug in inspectai currently misscores markdown-formatted multiple choice answers (e.g. ANSWER: $B$ rather than ANSWER: B), which the Inkling models often include in their response. Scores are manually evaluated to account for discrepancy. More information at https://github.com/UKGovernmentBEIS/inspect_ai/issues/5145

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Safetensors

Model size

952B params

Tensor type

F32

BF16

F8_E4M3

Model tree for RedHatAI/Inkling-NVFP4-FP8-BLOCK

Base model

thinkingmachines/Inkling

Quantized

(49)

this model