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README.md

@@ -1,3 +1,201 @@
----
-license: mit
----
+---
+license: mit
+language:
+- zh
+- en
+- fr
+- es
+- ru
+- de
+- ja
+- ko
+pipeline_tag: image-to-text
+library_name: transformers
+---
+
+# GLM-OCR
+
+<div align="center">
+<img src=https://raw.githubusercontent.com/zai-org/GLM-OCR/refs/heads/main/resources/logo.svg width="40%"/>
+</div>
+<p align="center">
+    👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-OCR/refs/heads/main/resources/wechat.png" target="_blank">WeChat</a> and <a href="https://discord.gg/8KFjEec7" target="_blank">Discord</a> community
+    <br>
+    📍 Use GLM-OCR's <a href="https://docs.z.ai/guides/image/glm-ocr" target="_blank">API</a>
+</p>
+
+
+## Introduction
+
+GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization. The model integrates the CogViT visual encoder pre-trained on large-scale image–text data, a lightweight cross-modal connector with efficient token downsampling, and a GLM-0.5B language decoder. Combined with a two-stage pipeline of layout analysis and parallel recognition based on PP-DocLayout-V3, GLM-OCR delivers robust and high-quality OCR performance across diverse document layouts.
+
+**Key Features**
+
+- **State-of-the-Art Performance**: Achieves a score of 94.62 on OmniDocBench V1.5, ranking #1 overall, and delivers state-of-the-art results across major document understanding benchmarks, including formula recognition, table recognition, and information extraction.
+
+- **Optimized for Real-World Scenarios**: Designed and optimized for practical business use cases, maintaining robust performance on complex tables, code-heavy documents, seals, and other challenging real-world layouts.
+
+- **Efficient Inference**: With only 0.9B parameters, GLM-OCR supports deployment via vLLM, SGLang, and Ollama, significantly reducing inference latency and compute cost, making it ideal for high-concurrency services and edge deployments.
+
+- **Easy to Use**: Fully open-sourced and equipped with a comprehensive [SDK](https://github.com/zai-org/GLM-OCR) and inference toolchain, offering simple installation, one-line invocation, and smooth integration into existing production pipelines.
+
+## Usage
+
+### vLLM
+
+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
+```
+
+### SGLang
+
+
+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
+```
+
+### Ollama 
+
+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
+```
+
+### Transformers
+
+```
+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)
+```
+
+### Prompt Limited
+
+GLM-OCR currently supports two types of prompt scenarios:
+
+1. **Document Parsing** – extract raw content from documents. Supported tasks include:
+
+```python
+{
+    "text": "Text Recognition:",
+    "formula": "Formula Recognition:",
+    "table": "Table Recognition:"
+}
+```
+
+2. **Information Extraction** – extract structured information from documents. Prompts must follow a strict JSON schema. For example, to extract personal ID information:
+
+```python
+请按下列JSON格式输出图中信息:
+{
+    "id_number": "",
+    "last_name": "",
+    "first_name": "",
+    "date_of_birth": "",
+    "address": {
+        "street": "",
+        "city": "",
+        "state": "",
+        "zip_code": ""
+    },
+    "dates": {
+        "issue_date": "",
+        "expiration_date": ""
+    },
+    "sex": ""
+}
+```
+
+⚠️ Note: When using information extraction, the output must strictly adhere to the defined JSON schema to ensure downstream processing compatibility.
+
+## GLM-OCR SDK
+
+We provide an easy-to-use SDK for using GLM-OCR more efficiently and conveniently. please check our [github](https://github.com/zai-org/GLM-OCR) to get more detail.
+
+## Acknowledgement
+
+This project is inspired by the excellent work of the following projects and communities:
+
+- [PP-DocLayout-V3](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3)
+- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
+- [MinerU](https://github.com/opendatalab/MinerU)
+
+## License
+
+The GLM-OCR model is released under the MIT License.
+
+The complete OCR pipeline integrates [PP-DocLayoutV3](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3) for document layout analysis, which is licensed under the Apache License 2.0. Users should comply with both licenses when using this project.

chat_template.jinja

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+[gMASK]<sop>
+{%- if tools -%}
+<|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{% for tool in tools %}
+{{ tool | tojson(ensure_ascii=False) }}
+{% endfor %}
+</tools>
+
+For each function call, output the function name and arguments within the following XML format:
+<tool_call>{function-name}
+<arg_key>{arg-key-1}</arg_key>
+<arg_value>{arg-value-1}</arg_value>
+<arg_key>{arg-key-2}</arg_key>
+<arg_value>{arg-value-2}</arg_value>
+...
+</tool_call>{%- endif -%}
+{%- macro visible_text(content) -%}
+    {%- if content is string -%}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping -%}
+        {%- for item in content -%}
+            {%- if item is mapping and item.type == 'text' -%}
+                {{- item.text }}
+            {%- elif item is mapping and (item.type == 'image' or 'image' in item) -%}
+                <|begin_of_image|><|image|><|end_of_image|>
+            {%- elif item is mapping and (item.type == 'video' or 'video' in item) -%}
+                <|begin_of_video|><|video|><|end_of_video|>
+            {%- elif item is string -%}
+                {{- item }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{- content }}
+    {%- endif -%}
+{%- endmacro -%}
+{%- set ns = namespace(last_user_index=-1) %}
+{%- for m in messages %}
+    {%- if m.role == 'user' %}
+        {% set ns.last_user_index = loop.index0 -%}
+    {%- endif %}
+{%- endfor %}
+{% for m in messages %}
+{%- if m.role == 'user' -%}<|user|>
+{% if m.content is string %}
+{{ m.content }}
+{%- else %}
+{%- for item in m.content %}
+{% if item.type == 'video' or 'video' in item %}
+<|begin_of_video|><|video|><|end_of_video|>{% elif item.type == 'image' or 'image' in item %}
+<|begin_of_image|><|image|><|end_of_image|>{% elif item.type == 'text' %}
+{{ item.text }}
+{%- endif %}
+{%- endfor %}
+{%- endif %}
+{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("/nothink")) else '' -}}
+{%- elif m.role == 'assistant' -%}
+<|assistant|>
+{%- set reasoning_content = '' %}
+{%- set content = visible_text(m.content) %}
+{%- if m.reasoning_content is string %}
+    {%- set reasoning_content = m.reasoning_content %}
+{%- else %}
+    {%- if '</think>' in content %}
+        {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+        {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+    {%- endif %}
+{%- endif %}
+{%- if loop.index0 > ns.last_user_index and reasoning_content -%}
+{{ '\n<think>' + reasoning_content.strip() +  '</think>'}}
+{%- else -%}
+{{ '\n<think></think>' }}
+{%- endif -%}
+{%- if content.strip() -%}
+{{ '\n' + content.strip() }}
+{%- endif -%}
+{% if m.tool_calls %}
+{% for tc in m.tool_calls %}
+{%- if tc.function %}
+    {%- set tc = tc.function %}
+{%- endif %}
+{{ '\n<tool_call>' + tc.name }}
+{% set _args = tc.arguments %}
+{% for k, v in _args.items() %}
+<arg_key>{{ k }}</arg_key>
+<arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>
+{% endfor %}
+</tool_call>{% endfor %}
+{% endif %}
+{%- elif m.role == 'tool' -%}
+{%- if m.content is string -%}
+{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+    {{- '<|observation|>' }}
+{%- endif %}
+{{- '\n<tool_response>\n' }}
+{{- m.content }}
+{{- '\n</tool_response>' }}
+{% elif m.content is iterable and m.content is not mapping %}
+{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+{{- '<|observation|>' }}
+{%- endif %}
+{{- '\n<tool_response>\n' }}
+{%- for tr in m.content -%}
+  {%- if tr is mapping and tr.type is defined -%}
+    {%- set t = tr.type | lower -%}
+    {%- if t == 'text' and tr.text is defined -%}
+{{ tr.text }}
+    {%- elif t in ['image', 'image_url'] -%}
+<|begin_of_image|><|image|><|end_of_image|>
+    {%- elif t in ['video', 'video_url'] -%}
+<|begin_of_video|><|video|><|end_of_video|>
+    {%- else -%}
+{{ tr | tojson(ensure_ascii=False) }}
+    {%- endif -%}
+  {%- else -%}
+{{ tr.output if tr.output is defined else tr }}
+  {%- endif -%}
+{%- endfor -%}
+{{- '\n</tool_response>' }}
+{%- else -%}
+<|observation|>{% for tr in m.content %}
+
+<tool_response>
+{{ tr.output if tr.output is defined else tr }}
+</tool_response>{% endfor -%}
+{% endif -%}
+{%- elif m.role == 'system' -%}
+<|system|>
+{{ visible_text(m.content) }}
+{%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+<|assistant|>
+{{'<think></think>\n' if (enable_thinking is defined and not enable_thinking) else ''}}
+{%- endif -%}

config.json

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+{
+  "architectures": [
+    "GlmOcrForConditionalGeneration"
+  ],
+  "model_type": "glm_ocr",
+  "text_config": {
+    "model_type": "glm_ocr_text",
+    "pad_token_id": 59246,
+    "vocab_size": 59392,
+    "eos_token_id": [
+      59246,
+      59253
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 1536,
+    "initializer_range": 0.02,
+    "intermediate_size": 4608,
+    "max_position_embeddings": 131072,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 16,
+    "num_nextn_predict_layers": 1,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "dtype": "bfloat16",
+    "rope_parameters": {
+      "rope_type": "default",
+      "mrope_section": [
+        16,
+        24,
+        24
+      ],
+       "partial_rotary_factor": 1.0,
+       "rope_theta": 10000
+    },
+    "tie_word_embeddings": false,
+    "use_cache": true
+  },
+  "vision_config": {
+    "model_type": "glm_ocr_vision",
+    "hidden_size": 1024,
+    "depth": 24,
+    "num_heads": 16,
+    "attention_bias": true,
+    "intermediate_size": 4096,
+    "hidden_act": "silu",
+    "hidden_dropout_prob": 0.0,
+    "initializer_range": 0.02,
+    "image_size": 336,
+    "patch_size": 14,
+    "out_hidden_size": 1536,
+    "rms_norm_eps": 1e-05,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "image_start_token_id": 59256,
+  "image_end_token_id": 59257,
+  "video_start_token_id": 59258,
+  "video_end_token_id": 59259,
+  "image_token_id": 59280,
+  "video_token_id": 59281,
+  "transformers_version": "5.0.1dev0"
+}

configuration.json

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+{"framework":"Pytorch","task":"image-text-to-text"}

generation_config.json

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+{
+  "_from_model_config": true,
+  "do_sample": false,
+  "eos_token_id": [
+    59246,
+    59253
+  ],
+  "pad_token_id": 59246,
+  "transformers_version": "5.0.1dev0"
+}

preprocessor_config.json

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+{
+    "size": {"shortest_edge": 12544, "longest_edge": 9633792},
+    "do_rescale": true,
+    "patch_size": 14,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [0.48145466, 0.4578275, 0.40821073],
+    "image_std": [0.26862954, 0.26130258, 0.27577711],
+    "image_processor_type": "Glm46VImageProcessor",
+    "processor_class": "Glm46VProcessor"
+}

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": [
+    "<|endoftext|>",
+    "[MASK]",
+    "[gMASK]",
+    "[sMASK]",
+    "<sop>",
+    "<eop>",
+    "<|system|>",
+    "<|user|>",
+    "<|assistant|>",
+    "<|observation|>",
+    "<|begin_of_image|>",
+    "<|end_of_image|>",
+    "<|begin_of_video|>",
+    "<|end_of_video|>",
+    "<|begin_of_audio|>",
+    "<|end_of_audio|>",
+    "<|begin_of_transcription|>",
+    "<|end_of_transcription|>",
+    "<|code_prefix|>",
+    "<|code_middle|>",
+    "<|code_suffix|>",
+    "<think>",
+    "</think>",
+    "<tool_call>",
+    "</tool_call>",
+    "<tool_response>",
+    "</tool_response>",
+    "<arg_key>",
+    "</arg_key>",
+    "<arg_value>",
+    "</arg_value>",
+    "/nothink",
+    "<|begin_of_box|>",
+    "<|end_of_box|>",
+    "<|image|>",
+    "<|video|>"
+  ],
+  "is_local": true,
+  "model_max_length": 655380,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "processor_class": "Glm46VProcessor",
+  "tokenizer_class": "TokenizersBackend"
+}