diff --git a/README.md b/README.md index dc1b89d..1a06b9d 100644 --- a/README.md +++ b/README.md @@ -10,5 +10,149 @@ pinned: true license: creativeml-openrail-m short_description: Qwen VL 2B --- +# Qwen2-VL-OCR-2B-Instruct [ VL / OCR ] -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file +![aaaaaaaaaaa.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/s42kASSQCoJAyYMJkoEuD.png) + +The **Qwen2-VL-OCR-2B-Instruct** model is a fine-tuned version of **Qwen/Qwen2-VL-2B-Instruct**, tailored for tasks that involve **Optical Character Recognition (OCR)**, **image-to-text conversion**, and **math problem solving with LaTeX formatting**. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively. + +#### Key Enhancements: + +* **SoTA understanding of images of various resolution & ratio**: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. + +* **Understanding videos of 20min+**: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc. + +* **Agent that can operate your mobiles, robots, etc.**: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions. + +* **Multilingual Support**: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc. + +| **File Name** | **Size** | **Description** | **Upload Status** | +|---------------------------|------------|------------------------------------------------|-------------------| +| `.gitattributes` | 1.52 kB | Configures LFS tracking for specific model files. | Initial commit | +| `README.md` | 203 Bytes | Minimal details about the uploaded model. | Updated | +| `added_tokens.json` | 408 Bytes | Additional tokens used by the model tokenizer. | Uploaded | +| `chat_template.json` | 1.05 kB | Template for chat-based model input/output. | Uploaded | +| `config.json` | 1.24 kB | Model configuration metadata. | Uploaded | +| `generation_config.json` | 252 Bytes | Configuration for text generation settings. | Uploaded | +| `merges.txt` | 1.82 MB | BPE merge rules for tokenization. | Uploaded | +| `model.safetensors` | 4.42 GB | Serialized model weights in a secure format. | Uploaded (LFS) | +| `preprocessor_config.json`| 596 Bytes | Preprocessing configuration for input data. | Uploaded | +| `vocab.json` | 2.78 MB | Vocabulary file for tokenization. | Uploaded | + +--- +### Sample Inference with Doc + +![123.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/TlsmcTqoQMvaBhwo8tGeU.png) + +**📍Demo**: https://huggingface.co/prithivMLmods/Qwen2-VL-OCR-2B-Instruct/blob/main/Demo/ocrtest_qwen.ipynb +### How to Use + +```python +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from qwen_vl_utils import process_vision_info + +# default: Load the model on the available device(s) +model = Qwen2VLForConditionalGeneration.from_pretrained( + "prithivMLmods/Qwen2-VL-OCR-2B-Instruct", torch_dtype="auto", device_map="auto" +) + +# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. +# model = Qwen2VLForConditionalGeneration.from_pretrained( +# "prithivMLmods/Qwen2-VL-OCR-2B-Instruct", +# torch_dtype=torch.bfloat16, +# attn_implementation="flash_attention_2", +# device_map="auto", +# ) + +# default processer +processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-OCR-2B-Instruct") + +# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage. +# min_pixels = 256*28*28 +# max_pixels = 1280*28*28 +# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) + +messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", + }, + {"type": "text", "text": "Describe this image."}, + ], + } +] + +# Preparation for inference +text = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True +) +image_inputs, video_inputs = process_vision_info(messages) +inputs = processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", +) +inputs = inputs.to("cuda") + +# Inference: Generation of the output +generated_ids = model.generate(**inputs, max_new_tokens=128) +generated_ids_trimmed = [ + out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) +] +output_text = processor.batch_decode( + generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False +) +print(output_text) +``` +### Buf +```python + buffer = "" + for new_text in streamer: + buffer += new_text + # Remove <|im_end|> or similar tokens from the output + buffer = buffer.replace("<|im_end|>", "") + yield buffer +``` +### **Key Features** + +1. **Vision-Language Integration:** + - Combines **image understanding** with **natural language processing** to convert images into text. + +2. **Optical Character Recognition (OCR):** + - Extracts and processes textual information from images with high accuracy. + +3. **Math and LaTeX Support:** + - Solves math problems and outputs equations in **LaTeX format**. + +4. **Conversational Capabilities:** + - Designed to handle **multi-turn interactions**, providing context-aware responses. + +5. **Image-Text-to-Text Generation:** + - Inputs can include **images, text, or a combination**, and the model generates descriptive or problem-solving text. + +6. **Secure Weight Format:** + - Uses **Safetensors** for faster and more secure model weight loading. + +--- + +### **Training Details** + +- **Base Model:** [Qwen/Qwen2-VL-2B-Instruct](#) +- **Model Size:** + - 2.21 Billion parameters + - Optimized for **BF16** tensor type, enabling efficient inference. + +- **Specializations:** + - OCR tasks in images containing text. + - Mathematical reasoning and LaTeX output for equations. + +--- + + + +Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference