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

Libraries

How to use unsloth/Inkling-Small with Transformers:

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

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

Install from pip and serve model

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

SGLang

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

Inkling-Small / config.json

danielhanchen

Upload folder using huggingface_hub

a1cdb28 verified 2 days ago

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	{
	"architectures": [
	"InklingForConditionalGeneration"
	],
	"audio_bos_token_id": 200020,
	"audio_config": {
	"audio_mode": "dmel",
	"bias": false,
	"decoder_dmodel": 4096,
	"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": 4096,
	"use_audio_norm": true
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	"audio_token_id": 200053,
	"eos_token_id": 200006,
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	"hidden_act": "silu",
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	"log_scaling_alpha": 0.1,
	"log_scaling_n_floor": 128000,
	"logits_mup_width_multiplier": 16.0,
	"max_position_embeddings": 1048576,
	"mlp_layer_types": [
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	"dense",
	"sparse",
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	"model_type": "inkling_text",
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	"n_routed_experts": 256,
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	"route_scale": 8.0,
	"shared_expert_sink": true,
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	"transformers_version": "5.15.0.dev0",
	"unsloth_fixed": true,
	"vision_config": {
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	}