tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
e35b0fc verified | # HunyuanOCR: document parsing with Transformers | |
| This page is the Transformers contract of the HunyuanOCR 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 `tencent/HunyuanOCR`. The MIC runner runs the same blocks against each slim fixture and each 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 a document image | |
| ```py runnable:usage | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| import numpy as np | |
| import torch | |
| from transformers import AutoProcessor, HunYuanVLForConditionalGeneration, set_seed | |
| model_id = "tencent/HunyuanOCR" | |
| 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 = HunYuanVLForConditionalGeneration.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = HunYuanVLForConditionalGeneration.from_pretrained(model_id, **variant).to(device) # doc-builder: hide | |
| model.eval() | |
| # Two small synthetic "document" images: white page with a dark bar of text. | |
| def page(bar_y, bar_h): | |
| img = np.full((128, 128, 3), 255, dtype=np.uint8) | |
| img[bar_y:bar_y + bar_h, 16:112] = 0 | |
| return img | |
| images = [page(24, 16), page(80, 24)] | |
| prompt = "Extract the text from the image." | |
| messages = [ | |
| [{"role": "user", "content": [{"type": "image", "image": im}, {"type": "text", "text": prompt}]}] | |
| for im in images | |
| ] | |
| texts = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) | |
| inputs = processor(text=texts, images=images, return_tensors="pt", padding=True) | |
| inputs = inputs.to(device, dtype=model.dtype) | |
| with torch.no_grad(): | |
| out = model(**inputs) | |
| logits = out.logits | |
| # Left padding: probe the last (real) position of each row, never position 0. | |
| probe = logits[:, -1, :].float() | |
| probe_ids = probe.topk(5, dim=-1).indices | |
| probe_vals = probe.gather(-1, probe_ids) | |
| print(probe_ids.tolist()) | |
| ``` | |
| ## One training step | |
| ```py runnable:usage:2 | |
| # Language-model labels: copy input_ids and mask padded positions. | |
| labels = inputs["input_ids"].clone() | |
| if "attention_mask" in inputs: | |
| 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", | |
| ) | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "logits": {"probe": probe_vals.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)) | |
| ``` | |