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

Libraries

How to use AxionML/Inkling-Small-NVFP4 with Transformers:

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

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

Install from pip and serve model

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

SGLang

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

Inkling-Small-NVFP4 / README.md

vincentzed-hf

Mirror thinkingmachines/Inkling-Small-NVFP4@b6a9953446 with AxionML model card

c8332c6 verified 13 days ago

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metadata

pipeline_tag: image-text-to-text
base_model:
  - thinkingmachines/Inkling-Small
base_model_relation: quantized
license: apache-2.0
library_name: transformers
tags:
  - AxionML
  - ModelOpt
  - Inkling
  - quantized
  - NVFP4
  - nvfp4
  - sglang
  - mirror

AxionML Inkling-Small-NVFP4

Mirrored by AxionML for open-source serving and deployment use cases. Part of AxionML's effort to provide ready-to-serve quantized models for the community.

Quantized by Thinking Machines. The weights in this repository are an unmodified copy of thinkingmachines/Inkling-Small-NVFP4 (revision b6a99534467840620d411e4cd4ad5819b2610d9c). All credit for the quantization belongs to Thinking Machines.

This is an NVFP4-quantized version of thinkingmachines/Inkling-Small (276B total parameters, 12B activated; text, image and audio input), quantized with NVIDIA Model Optimizer.

About NVFP4 quantization: NVFP4 on Blackwell couples a compact E2M1 FP4 codebook with blockwise FP8 (E4M3) scaling over 16-element micro-blocks, so that 4-bit stored values remain numerically useful for neural-network computation. The E2M1 codebook provides a small, nonuniform set of representable magnitudes up to ±6 and relies on saturating behavior rather than IEEE NaN/Inf encodings to maximize usable range per bit. Using an FP8 block scale (rather than power-of-two-only E8M0) enables fractional scales and error-minimizing scale selection. On Blackwell Tensor Cores, native FP4 multipliers exploit E2M1 simplicity while higher-precision FP32 accumulation protects dot-product accuracy.

Ready for commercial and non-commercial use under Apache 2.0, subject to Thinking Machines' Acceptable Use Policy.

Model Summary


Architecture	42-layer decoder-only multimodal MoE; hybrid local/global attention
Total Parameters	276B
Activated Parameters	12B
Experts	256 routed (6 active) + 2 shared
Input	Text, image, audio (16 kHz WAV)
Checkpoint Size	~171 GB

Evaluation Results

Benchmark	Inkling-Small
SWE-bench Verified	80.2
SWE-bench Pro (Public)	55.9
Terminal-Bench 2.1 (best harness)	64.7
SciCode	48.7
GDPval-AA v2	1269
MCP Atlas (public)	79.6
BrowseComp (w/ context)	77.4
Toolathlon Verified	54.4
GPQA Diamond	89.5
HLE (text only)	31.6
HLE (with tools)	47.8

Scores are from the Inkling-Small model card (full-precision baseline).

Quantization Details

Quantization format: NVFP4 (group size 16), Thinking Machines' official NVFP4 release; attention, routers, vision/audio encoders and other excluded modules stay in BF16
KV cache: not quantized

Usage

Deploy with SGLang

python3 -m sglang.launch_server \
    --model-path AxionML/Inkling-Small-NVFP4 \
    --tp 4 \
    --trust-remote-code

Deploy with vLLM

vllm serve AxionML/Inkling-Small-NVFP4 \
    --tensor-parallel-size 4 \
    --trust-remote-code

Minimal commands; for tuned serving flags follow the official recipes: SGLang, vLLM, TokenSpeed.

Limitations

The base model was trained on data that may contain toxic language and societal biases. The quantized model inherits these limitations. It may generate inaccurate, biased, or offensive content. Please refer to the original model card and the upstream quantized model card for full details.

Credits

Base model: thinkingmachines/Inkling-Small
Quantization: thinkingmachines/Inkling-Small-NVFP4 by Thinking Machines
Mirror: AxionML