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

Libraries

How to use tiny-random/glm-ocr with Transformers:

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

pipe = pipeline("image-text-to-text", model="tiny-random/glm-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 AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("tiny-random/glm-ocr")
model = AutoModelForImageTextToText.from_pretrained("tiny-random/glm-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?"}
        ]
    },
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tiny-random/glm-ocr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/glm-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": "tiny-random/glm-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/tiny-random/glm-ocr

SGLang

How to use tiny-random/glm-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 "tiny-random/glm-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": "tiny-random/glm-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 "tiny-random/glm-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": "tiny-random/glm-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 tiny-random/glm-ocr with Docker Model Runner:
```
docker model run hf.co/tiny-random/glm-ocr
```

glm-ocr

File size: 1,656 Bytes

2fd105c

{
  "architectures": [
    "GlmOcrForConditionalGeneration"
  ],
  "dtype": "bfloat16",
  "image_end_token_id": 59257,
  "image_start_token_id": 59256,
  "image_token_id": 59280,
  "model_type": "glm_ocr",
  "text_config": {
    "attention_bias": false,
    "attention_dropout": 0.0,
    "dtype": "bfloat16",
    "eos_token_id": [
      59246,
      59253
    ],
    "head_dim": 32,
    "hidden_act": "silu",
    "hidden_size": 8,
    "initializer_range": 0.02,
    "intermediate_size": 64,
    "max_position_embeddings": 131072,
    "model_type": "glm_ocr_text",
    "num_attention_heads": 8,
    "num_hidden_layers": 2,
    "num_key_value_heads": 4,
    "num_nextn_predict_layers": 1,
    "pad_token_id": 59246,
    "rms_norm_eps": 1e-05,
    "rope_parameters": {
      "mrope_section": [
        4,
        4,
        8
      ],
      "partial_rotary_factor": 1.0,
      "rope_theta": 10000,
      "rope_type": "default"
    },
    "tie_word_embeddings": false,
    "use_cache": true,
    "vocab_size": 59392
  },
  "tie_word_embeddings": false,
  "transformers_version": "5.2.0.dev0",
  "video_end_token_id": 59259,
  "video_start_token_id": 59258,
  "video_token_id": 59281,
  "vision_config": {
    "attention_bias": true,
    "attention_dropout": 0.0,
    "depth": 2,
    "hidden_act": "silu",
    "hidden_dropout_prob": 0.0,
    "hidden_size": 32,
    "image_size": 336,
    "in_channels": 3,
    "initializer_range": 0.02,
    "intermediate_size": 64,
    "model_type": "glm_ocr_vision",
    "num_heads": 1,
    "out_hidden_size": 8,
    "patch_size": 14,
    "rms_norm_eps": 1e-05,
    "spatial_merge_size": 2,
    "temporal_patch_size": 2
  }
}