Instructions to use Devique/Calmiq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Devique/Calmiq with Transformers:

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

pipe = pipeline("image-text-to-text", model="Devique/Calmiq")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Devique/Calmiq")
model = AutoModelForImageTextToText.from_pretrained("Devique/Calmiq")
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 Devique/Calmiq with vLLM:

Install from pip and serve model

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

SGLang

How to use Devique/Calmiq 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 "Devique/Calmiq" \
    --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": "Devique/Calmiq",
		"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 "Devique/Calmiq" \
        --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": "Devique/Calmiq",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use Devique/Calmiq with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Devique/Calmiq to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Devique/Calmiq to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Devique/Calmiq to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Devique/Calmiq",
    max_seq_length=2048,
)

Docker Model Runner
How to use Devique/Calmiq with Docker Model Runner:
```
docker model run hf.co/Devique/Calmiq
```

EddySL commited on Aug 11, 2025

Commit

394c684

verified ·

1 Parent(s): 42ab58a

(Trained with Unsloth)

Browse files

Files changed (1) hide show

config.json +4 -4

config.json CHANGED Viewed

@@ -42,7 +42,7 @@
                 2
             ]
         ],
-        "torch_dtype": "bfloat16",
         "vocab_offset": 262272,
         "vocab_size": 128
     },
@@ -194,12 +194,12 @@
         "rope_scaling": null,
         "rope_theta": 1000000.0,
         "sliding_window": 512,
-        "torch_dtype": "bfloat16",
         "use_cache": true,
         "vocab_size": 262400,
         "vocab_size_per_layer_input": 262144
     },
-    "torch_dtype": "bfloat16",
     "transformers_version": "4.55.0",
     "unsloth_fixed": true,
     "unsloth_version": "2025.8.4",
@@ -216,7 +216,7 @@
         "model_type": "gemma3n_vision",
         "num_classes": 2,
         "rms_norm_eps": 1e-06,
-        "torch_dtype": "bfloat16",
         "vocab_offset": 262144,
         "vocab_size": 128
     },

                 2
             ]
         ],
+        "torch_dtype": "float16",
         "vocab_offset": 262272,
         "vocab_size": 128
     },
         "rope_scaling": null,
         "rope_theta": 1000000.0,
         "sliding_window": 512,
+        "torch_dtype": "float16",
         "use_cache": true,
         "vocab_size": 262400,
         "vocab_size_per_layer_input": 262144
     },
+    "torch_dtype": "float16",
     "transformers_version": "4.55.0",
     "unsloth_fixed": true,
     "unsloth_version": "2025.8.4",
         "model_type": "gemma3n_vision",
         "num_classes": 2,
         "rms_norm_eps": 1e-06,
+        "torch_dtype": "float16",
         "vocab_offset": 262144,
         "vocab_size": 128
     },