# GLM-OCR: image-to-text with Transformers

This page is the Transformers contract of the GLM-OCR MIC and also a usage example. [doc-builder](https://github.com/huggingface/doc-builder/blob/main/docs/runnable-code-blocks.md) renders the `runnable` blocks below as documentation, and its pytest plugin runs them as written against `zai-org/GLM-OCR`. The MIC runner runs the same blocks against the slim fixture and the checkpoint in [`integration.json`](../../integration.json). Lines marked `# doc-builder: hide` only matter there: they swap in the pinned checkpoint or variant and print the result record.

## Read text from an image

```py runnable:ocr
import torch
from PIL import Image, ImageDraw
from transformers import AutoModelForImageTextToText, AutoProcessor, set_seed
import json  # doc-builder: hide
import os  # doc-builder: hide

model_id = "zai-org/GLM-OCR"
model_id = os.environ.get("MIC_MODEL", model_id)  # doc-builder: hide
variant = json.loads(os.environ.get("MIC_MODEL_KWARGS", "{}"))  # doc-builder: hide
device = "cuda" if torch.cuda.is_available() else "cpu"
set_seed(0)  # seeds torch, NumPy, and Python: loading, dropout, and data augmentation draw random numbers

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id).to(device)
if variant:  # doc-builder: hide
    del model  # doc-builder: hide
    model = AutoModelForImageTextToText.from_pretrained(model_id, **variant).to(device)  # doc-builder: hide
model.eval()

# Two small synthetic document images so the batch perturbation is meaningful.
def make_doc(text):
    image = Image.new("RGB", (224, 96), "white")
    ImageDraw.Draw(image).text((8, 40), text, fill="black")
    return image

images = [make_doc("INVOICE 42"), make_doc("TOTAL 7.50")]
messages = [[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Read the text in this image."}]}] for _ in images]
texts = [processor.apply_chat_template(m, add_generation_prompt=True, tokenize=False) for m in messages]
inputs = processor(text=texts, images=images, padding=True, return_tensors="pt")
inputs = inputs.to(device, dtype=model.dtype)

with torch.no_grad():
    logits = model(**inputs).logits
# With right padding the last real token of each row is at its attention_mask sum - 1.
last = inputs["attention_mask"].sum(dim=-1) - 1
probe_logits = logits[torch.arange(logits.shape[0]), last].float()
predicted = probe_logits.argmax(dim=-1)
print(processor.tokenizer.convert_ids_to_tokens(predicted))
```

## One training step

```py runnable:ocr:2
# Language-model labels: copy the input ids and mask padded positions.
labels = inputs["input_ids"].clone()
labels[inputs["attention_mask"] == 0] = -100

model.train()
optimizer = torch.optim.SGD([p for p in model.parameters() if p.requires_grad], lr=0.01)
loss = model(**inputs, labels=labels).loss
loss.backward()
before = {n: p.detach().cpu().clone() for n, p in model.named_parameters() if p.requires_grad}  # doc-builder: hide
optimizer.step()
print(f"loss: {loss.item():.4f}")

if os.environ.get("MIC_RUNNER"):  # doc-builder: hide
    named = dict(model.named_parameters())
    checked = (
        "model.language_model.layers.0.self_attn.q_proj.weight",
        "model.language_model.layers.0.mlp.down_proj.weight",
        "model.visual.blocks.0.attn.qkv.weight",
    )
    result = {
        "runtime": {
            "device": model.device.type,
            "dtype": str(model.dtype).removeprefix("torch."),
            "attn_implementation": model.config._attn_implementation,
        },
        "predicted_tokens": predicted.tolist(),
        "answer_logits": probe_logits[:, predicted].tolist(),
        "training": {
            "loss": loss.item(),
            "gradients": {n: bool(named[n].grad is not None and torch.count_nonzero(named[n].grad) > 0 and torch.isfinite(named[n].grad).all()) for n in checked},
            "updated": any(not torch.equal(p.detach().cpu(), before[n]) for n, p in model.named_parameters() if n in before),
        },
    }
    print(json.dumps(result))
```