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

Libraries

How to use thinkingmachines/Inkling-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="thinkingmachines/Inkling-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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("thinkingmachines/Inkling-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("thinkingmachines/Inkling-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?"}
        ]
    },
]
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 thinkingmachines/Inkling-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thinkingmachines/Inkling-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": "thinkingmachines/Inkling-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/thinkingmachines/Inkling-NVFP4

SGLang

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

Inkling-NVFP4 / config.json

aurick

Model release

93a182f about 22 hours ago

Raw

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2.42 kB

	{
	"architectures": [
	"InklingForConditionalGeneration"
	],
	"model_type": "inkling_mm_model",
	"eos_token_id": 200006,
	"text_config": {
	"model_max_length": 1048576,
	"torch_dtype": "bfloat16",
	"hidden_size": 6144,
	"num_hidden_layers": 66,
	"vocab_size": 201024,
	"num_attention_heads": 64,
	"num_key_value_heads": 8,
	"head_dim": 128,
	"d_rel": 16,
	"rel_extent": 1024,
	"q_bias": false,
	"o_bias": false,
	"log_scaling_n_floor": 128000,
	"log_scaling_alpha": 0.1,
	"rms_norm_eps": 1e-06,
	"use_embed_norm": true,
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	"dense_mlp_idx": 2,
	"use_sconv": true,
	"sconv_kernel_size": 4,
	"unpadded_vocab_size": 200058,
	"logits_mup_width_multiplier": 24.0,
	"final_logit_softcapping": null,
	"swa_head_dim": 128,
	"swa_num_attention_heads": 64,
	"swa_num_key_value_heads": 16,
	"sliding_window_size": 512,
	"n_routed_experts": 256,
	"num_experts_per_tok": 6,
	"n_shared_experts": 2,
	"shared_expert_sink": true,
	"dense_intermediate_size": 24576,
	"intermediate_size": 3072,
	"route_scale": 8.0,
	"use_gate_bias": true,
	"gate_activation": "sigmoid",
	"norm_after_topk": true,
	"use_global_scale": true
	},
	"audio_config": {
	"decoder_dmodel": 6144,
	"n_mel_bins": 80,
	"mel_vocab_size": 16,
	"bias": false,
	"dmel_min_value": -7.0,
	"dmel_max_value": 2.0,
	"use_audio_norm": true,
	"audio_mode": "dmel"
	},
	"vision_config": {
	"vision_encoder_type": "hmlp",
	"decoder_dmodel": 6144,
	"patch_size": 40,
	"temporal_patch_size": 2,
	"n_channels": 3,
	"n_layers": 4,
	"use_vision_norm": true
	},
	"mtp_config": {
	"num_nextn_predict_layers": 8,
	"chain_hidden_post_norm": false,
	"local_layer_ids": [
	0,
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	]
	}
	}