Instructions to use BDRC/tibetan-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BDRC/tibetan-ocr with Transformers:

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

pipe = pipeline("image-text-to-text", model="BDRC/tibetan-ocr")
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("BDRC/tibetan-ocr")
model = AutoModelForMultimodalLM.from_pretrained("BDRC/tibetan-ocr", 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 BDRC/tibetan-ocr with vLLM:

Install from pip and serve model

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

SGLang

How to use BDRC/tibetan-ocr 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 "BDRC/tibetan-ocr" \
    --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": "BDRC/tibetan-ocr",
		"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 "BDRC/tibetan-ocr" \
        --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": "BDRC/tibetan-ocr",
		"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 BDRC/tibetan-ocr with Docker Model Runner:
```
docker model run hf.co/BDRC/tibetan-ocr
```

tibetan-ocr / chat_template.jinja

Eroux

Yigdzin 1 (v1): grow26-ep2 checkpoint + model card (private test upload)

b1d6115 verified 10 days ago

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	{%- if not add_generation_prompt is defined -%}
	{%- set add_generation_prompt = true -%}
	{%- endif -%}
	{%- if not cls_token is defined -%}
	{%- set cls_token = "<\|begin_of_sentence\|>" -%}
	{%- endif -%}
	{%- if not eos_token is defined -%}
	{%- set eos_token = "</s>" -%}
	{%- endif -%}
	{{- cls_token -}}
	{%- for message in messages -%}
	{%- if message["role"] == "user" -%}
	{{- "User: " -}}
	{%- for content in message["content"] -%}
	{%- if content["type"] == "image" -%}
	{{ "<\|IMAGE_START\|><\|IMAGE_PLACEHOLDER\|><\|IMAGE_END\|>" }}
	{%- endif -%}
	{%- endfor -%}
	{%- for content in message["content"] -%}
	{%- if content["type"] == "text" -%}
	{{ content["text"] }}
	{%- endif -%}
	{%- endfor -%}
	{{ "\n" -}}
	{%- elif message["role"] == "assistant" -%}
	{{- "Assistant:\n" -}}
	{%- for content in message["content"] -%}
	{%- if content["type"] == "text" -%}
	{{ content["text"] }}
	{%- endif -%}
	{%- endfor -%}
	{{ eos_token -}}
	{%- elif message["role"] == "system" -%}
	{%- for content in message["content"] -%}
	{%- if content["type"] == "text" -%}
	{{ content["text"] + "\n" }}
	{%- endif -%}
	{%- endfor -%}
	{%- endif -%}
	{%- endfor -%}
	{%- if add_generation_prompt -%}
	{{- "Assistant:\n" -}}
	{%- endif -%}