Compare model versions

Diff

Before

license: mit
language:
- zh
- en
- fr
- es
- ru
- de
- ja
- ko
library_name: transformers

After

license: mit
language:
- zh
- en
- fr
- es
- ru
- de
- ja
- ko
pipeline_tag: image-to-text
library_name: transformers
tags:
- conversational

Diff

Before

Not present in the earlier snapshot.

After

GLM-OCR supports deployment with the following frameworks. Feel free to try them out:

- [SGLang](https://github.com/sgl-project/sglang) — see [cookbook](https://cookbook.sglang.io/autoregressive/GLM/GLM-OCR)
- [vLLM](https://github.com/vllm-project/vllm) — see [recipes](https://recipes.vllm.ai/zai-org/GLM-OCR)
- [Transformers](https://github.com/huggingface/transformers) — see [transformers docs](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/glm_ocr.md)

Diff

Before

1. run 

```bash
pip install -U vllm --extra-index-url https://wheels.vllm.ai/nightly
```

or using docker with:
```
docker pull vllm/vllm-openai:nightly
```

2. run with:

```bash
pip install git+https://github.com/huggingface/transformers.git
vllm serve zai-org/GLM-OCR  --allowed-local-media-path /  --port 8080
```

After

Not present in the later snapshot.

Diff

Before

1. using docker with:

```bash
docker pull lmsysorg/sglang:dev
```

or build it from source with:

```bash
pip install git+https://github.com/sgl-project/sglang.git#subdirectory=python
```

2. run with:

```bash
pip install git+https://github.com/huggingface/transformers.git
python -m sglang.launch_server --model zai-org/GLM-OCR --port 8080
```

After

Not present in the later snapshot.

Diff

Before

1. Download [Ollama](https://ollama.com/download).
2. run with: 

```bash
ollama run glm-ocr
```

Ollama will automatically use image file path when an image is dragged into the terminal: 

```bash
ollama run glm-ocr Text Recognition: ./image.png
```

After

Not present in the later snapshot.

Diff

Before

```
pip install git+https://github.com/huggingface/transformers.git
```

```python
from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

MODEL_PATH = "zai-org/GLM-OCR"
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": "test_image.png"
            },
            {
                "type": "text",
                "text": "Text Recognition:"
            }
        ],
    }
]
processor = AutoProcessor.from_pretrained(MODEL_PATH)
model = AutoModelForImageTextToText.from_pretrained(
    pretrained_model_name_or_path=MODEL_PATH,
    torch_dtype="auto",
    device_map="auto",
)
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)
inputs.pop("token_type_ids", None)
generated_ids = model.generate(**inputs, max_new_tokens=8192)
output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
print(output_text)
```

After

Not present in the later snapshot.