Instructions to use Ba2han/Liquid-Turkish-MiniOCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ba2han/Liquid-Turkish-MiniOCR with Transformers:

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

pipe = pipeline("image-text-to-text", model="Ba2han/Liquid-Turkish-MiniOCR")
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("Ba2han/Liquid-Turkish-MiniOCR")
model = AutoModelForImageTextToText.from_pretrained("Ba2han/Liquid-Turkish-MiniOCR")
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

vLLM

How to use Ba2han/Liquid-Turkish-MiniOCR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ba2han/Liquid-Turkish-MiniOCR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ba2han/Liquid-Turkish-MiniOCR",
		"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/Ba2han/Liquid-Turkish-MiniOCR

SGLang

How to use Ba2han/Liquid-Turkish-MiniOCR 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 "Ba2han/Liquid-Turkish-MiniOCR" \
    --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": "Ba2han/Liquid-Turkish-MiniOCR",
		"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 "Ba2han/Liquid-Turkish-MiniOCR" \
        --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": "Ba2han/Liquid-Turkish-MiniOCR",
		"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 new

How to use Ba2han/Liquid-Turkish-MiniOCR 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 Ba2han/Liquid-Turkish-MiniOCR 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 Ba2han/Liquid-Turkish-MiniOCR to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ba2han/Liquid-Turkish-MiniOCR to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Ba2han/Liquid-Turkish-MiniOCR",
    max_seq_length=2048,
)

Docker Model Runner
How to use Ba2han/Liquid-Turkish-MiniOCR with Docker Model Runner:
```
docker model run hf.co/Ba2han/Liquid-Turkish-MiniOCR
```

Ba2han commited on Apr 13

Commit

b80cad2

verified ·

1 Parent(s): 5ace49c

Update processor_config.json

Browse files

Files changed (1) hide show

processor_config.json +6 -6

processor_config.json CHANGED Viewed

@@ -13,27 +13,27 @@
       0.5,
       0.5
     ],
-    "image_processor_type": "Lfm2VlImageProcessor",
     "image_std": [
       0.5,
       0.5,
       0.5
     ],
-    "max_image_tokens": 768,
     "max_num_patches": 1024,
     "max_pixels_tolerance": 2.0,
-    "max_tiles": 10,
     "min_image_tokens": 64,
     "min_tiles": 2,
     "resample": 2,
     "rescale_factor": 0.00392156862745098,
     "return_row_col_info": true,
     "size": {
-      "height": 1024,
-      "width": 1024
     },
     "tile_size": 512,
     "use_thumbnail": true
   },
   "processor_class": "Lfm2VlProcessor"
-}

       0.5,
       0.5
     ],
+    "image_processor_type": "Lfm2VlImageProcessorFast",
     "image_std": [
       0.5,
       0.5,
       0.5
     ],
+    "max_image_tokens": 256,
     "max_num_patches": 1024,
     "max_pixels_tolerance": 2.0,
+    "max_tiles": 20,
     "min_image_tokens": 64,
     "min_tiles": 2,
     "resample": 2,
     "rescale_factor": 0.00392156862745098,
     "return_row_col_info": true,
     "size": {
+      "height": 512,
+      "width": 512
     },
     "tile_size": 512,
     "use_thumbnail": true
   },
   "processor_class": "Lfm2VlProcessor"
+}