Instructions to use tiny-random/inkling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/inkling with Transformers:

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

pipe = pipeline("image-text-to-text", model="tiny-random/inkling")
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("tiny-random/inkling")
model = AutoModelForMultimodalLM.from_pretrained("tiny-random/inkling", 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 tiny-random/inkling with vLLM:

Install from pip and serve model

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

SGLang

How to use tiny-random/inkling 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 "tiny-random/inkling" \
    --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": "tiny-random/inkling",
		"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 "tiny-random/inkling" \
        --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": "tiny-random/inkling",
		"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 tiny-random/inkling with Docker Model Runner:
```
docker model run hf.co/tiny-random/inkling
```

inkling

File size: 2,829 Bytes

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{
  "architectures": [
    "InklingForConditionalGeneration"
  ],
  "audio_bos_token_id": 200020,
  "audio_config": {
    "audio_mode": "dmel",
    "bias": false,
    "decoder_dmodel": 8,
    "dmel_max_value": 2.0,
    "dmel_min_value": -7.0,
    "initializer_range": 0.02,
    "mel_vocab_size": 16,
    "model_type": "inkling_audio",
    "n_mel_bins": 80,
    "rms_norm_eps": 1e-06,
    "text_hidden_size": 8,
    "use_audio_norm": true
  },
  "audio_token_id": 200053,
  "dtype": "bfloat16",
  "eos_token_id": 200006,
  "image_bos_token_id": 200005,
  "image_token_id": 200054,
  "model_type": "inkling_mm_model",
  "mtp_config": {
    "chain_hidden_post_norm": false,
    "local_layer_ids": [
      0
    ],
    "num_nextn_predict_layers": 1
  },
  "text_config": {
    "attention_dropout": 0.0,
    "bos_token_id": 1,
    "chain_hidden_post_norm": false,
    "conv_kernel_size": 4,
    "d_rel": 16,
    "dtype": "bfloat16",
    "eos_token_id": 2,
    "final_logit_softcapping": null,
    "gate_activation": "sigmoid",
    "head_dim": 32,
    "hidden_act": "silu",
    "hidden_size": 8,
    "initializer_range": 0.02,
    "intermediate_size": 32,
    "layer_types": [
      "hybrid_sliding",
      "hybrid"
    ],
    "local_layer_ids": [
      0
    ],
    "log_scaling_alpha": 0.1,
    "log_scaling_n_floor": 128000,
    "logits_mup_width_multiplier": 24.0,
    "max_position_embeddings": 1048576,
    "mlp_layer_types": [
      "dense",
      "sparse"
    ],
    "model_type": "inkling_text",
    "moe_intermediate_size": 32,
    "mtp_hidden_states_first": true,
    "mtp_local_layer_ids": [
      0
    ],
    "n_routed_experts": 256,
    "n_shared_experts": 2,
    "norm_after_topk": true,
    "num_attention_heads": 8,
    "num_experts_per_tok": 6,
    "num_hidden_layers": 2,
    "num_key_value_heads": 4,
    "num_mtp_layers": 1,
    "number_of_conv_states": 4,
    "o_bias": false,
    "pad_token_id": null,
    "q_bias": false,
    "rel_extent": 1024,
    "rms_norm_eps": 1e-06,
    "rms_norm_eps_moe_gate": 1e-06,
    "route_scale": 8.0,
    "shared_expert_sink": true,
    "sliding_window_size": 512,
    "swa_head_dim": 32,
    "swa_num_attention_heads": 8,
    "swa_num_key_value_heads": 4,
    "unpadded_vocab_size": 200058,
    "use_embed_norm": true,
    "use_gate_bias": true,
    "use_global_scale": true,
    "use_sconv": true,
    "vocab_size": 201024
  },
  "transformers_version": "5.15.0.dev0",
  "vision_config": {
    "decoder_dmodel": 8,
    "hidden_size": 1024,
    "initializer_range": 0.02,
    "model_type": "inkling_vision",
    "n_channels": 3,
    "n_layers": 2,
    "num_attention_heads": 16,
    "num_channels": 3,
    "patch_size": 40,
    "rms_norm_eps": 1e-06,
    "temporal_patch_size": 2,
    "text_hidden_size": 8,
    "use_vision_norm": true,
    "vision_encoder_type": "hmlp"
  }
}