Instructions to use abacusai/Smaug-Humanizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abacusai/Smaug-Humanizer with Transformers:

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

pipe = pipeline("text-generation", model="abacusai/Smaug-Humanizer")
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("abacusai/Smaug-Humanizer")
model = AutoModelForMultimodalLM.from_pretrained("abacusai/Smaug-Humanizer", 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 abacusai/Smaug-Humanizer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abacusai/Smaug-Humanizer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abacusai/Smaug-Humanizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abacusai/Smaug-Humanizer

SGLang

How to use abacusai/Smaug-Humanizer 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 "abacusai/Smaug-Humanizer" \
    --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": "abacusai/Smaug-Humanizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "abacusai/Smaug-Humanizer" \
        --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": "abacusai/Smaug-Humanizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abacusai/Smaug-Humanizer with Docker Model Runner:
```
docker model run hf.co/abacusai/Smaug-Humanizer
```

Smaug-Humanizer / tokenizer_config.json

plslokeshreddy

Initial release

b08d037 4 days ago

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1.48 kB

{
  "audio_token": "<|audio|>",
  "backend": "tokenizers",
  "boa_token": "<|audio>",
  "boi_token": "<|image>",
  "bos_token": "<bos>",
  "eoa_token": "<audio|>",
  "eoc_token": "<channel|>",
  "eoi_token": "<image|>",
  "eos_token": "<eos>",
  "eot_token": "<turn|>",
  "escape_token": "<|\"|>",
  "etc_token": "<tool_call|>",
  "etd_token": "<tool|>",
  "etr_token": "<tool_response|>",
  "extra_special_tokens": [
    "<|video|>"
  ],
  "image_token": "<|image|>",
  "mask_token": "<mask>",
  "model_max_length": 1000000000000000019884624838656,
  "model_specific_special_tokens": {
    "audio_token": "<|audio|>",
    "boa_token": "<|audio>",
    "boi_token": "<|image>",
    "eoa_token": "<audio|>",
    "eoc_token": "<channel|>",
    "eoi_token": "<image|>",
    "eot_token": "<turn|>",
    "escape_token": "<|\"|>",
    "etc_token": "<tool_call|>",
    "etd_token": "<tool|>",
    "etr_token": "<tool_response|>",
    "image_token": "<|image|>",
    "soc_token": "<|channel>",
    "sot_token": "<|turn>",
    "stc_token": "<|tool_call>",
    "std_token": "<|tool>",
    "str_token": "<|tool_response>",
    "think_token": "<|think|>"
  },
  "pad_token": "<pad>",
  "padding_side": "left",
  "processor_class": "Gemma4UnifiedProcessor",
  "soc_token": "<|channel>",
  "sot_token": "<|turn>",
  "stc_token": "<|tool_call>",
  "std_token": "<|tool>",
  "str_token": "<|tool_response>",
  "think_token": "<|think|>",
  "tokenizer_class": "GemmaTokenizer",
  "unk_token": "<unk>"
}

	{
	"audio_token": "<\|audio\|>",
	"backend": "tokenizers",
	"boa_token": "<\|audio>",
	"boi_token": "<\|image>",
	"bos_token": "<bos>",
	"eoa_token": "<audio\|>",
	"eoc_token": "<channel\|>",
	"eoi_token": "<image\|>",
	"eos_token": "<eos>",
	"eot_token": "<turn\|>",
	"escape_token": "<\|\"\|>",
	"etc_token": "<tool_call\|>",
	"etd_token": "<tool\|>",
	"etr_token": "<tool_response\|>",
	"extra_special_tokens": [
	"<\|video\|>"
	],
	"image_token": "<\|image\|>",
	"mask_token": "<mask>",
	"model_max_length": 1000000000000000019884624838656,
	"model_specific_special_tokens": {
	"audio_token": "<\|audio\|>",
	"boa_token": "<\|audio>",
	"boi_token": "<\|image>",
	"eoa_token": "<audio\|>",
	"eoc_token": "<channel\|>",
	"eoi_token": "<image\|>",
	"eot_token": "<turn\|>",
	"escape_token": "<\|\"\|>",
	"etc_token": "<tool_call\|>",
	"etd_token": "<tool\|>",
	"etr_token": "<tool_response\|>",
	"image_token": "<\|image\|>",
	"soc_token": "<\|channel>",
	"sot_token": "<\|turn>",
	"stc_token": "<\|tool_call>",
	"std_token": "<\|tool>",
	"str_token": "<\|tool_response>",
	"think_token": "<\|think\|>"
	},
	"pad_token": "<pad>",
	"padding_side": "left",
	"processor_class": "Gemma4UnifiedProcessor",
	"soc_token": "<\|channel>",
	"sot_token": "<\|turn>",
	"stc_token": "<\|tool_call>",
	"std_token": "<\|tool>",
	"str_token": "<\|tool_response>",
	"think_token": "<\|think\|>",
	"tokenizer_class": "GemmaTokenizer",
	"unk_token": "<unk>"
	}