134 lines
5.0 KiB
Markdown
134 lines
5.0 KiB
Markdown
---
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license: apache-2.0
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pipeline_tag: image-text-to-text
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datasets:
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- linxy/LaTeX_OCR
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- unsloth/LaTeX_OCR
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- v1v1d/Latexify_v1
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- lamm-mit/OleehyO-latex-formulas
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen2-VL-2B-Instruct
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library_name: transformers
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tags:
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- text-generation-inference
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- ocr
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- vl
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- qwen2_vl
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- 2B
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- VQA
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- KIE
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- Latex
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---
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# **Qwen2-VL-OCR2-2B-Instruct [ VL / OCR ]**
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# **Qwen2-VL-OCR2-2B-Instruct**
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The **Qwen2-VL-OCR2-2B-Instruct** model is a fine-tuned version of **Qwen/Qwen2-VL-2B-Instruct**, tailored for tasks that involve **Optical Character Recognition (OCR)**, **English language understanding**, and **math problem solving with LaTeX formatting**. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively.
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> [!note]
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> previous version https://huggingface.co/prithivMLmods/Qwen2-VL-OCR-2B-Instruct
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#### Key Enhancements:
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* **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.
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* **Understanding videos of 20min+**: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.
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* **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.
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* **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.
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### How to Use
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```python
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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# default: Load the model on the available device(s)
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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"prithivMLmods/Qwen2-VL-OCR2-2B-Instruct", torch_dtype="auto", device_map="auto"
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)
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# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
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# model = Qwen2VLForConditionalGeneration.from_pretrained(
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# "prithivMLmods/Qwen2-VL-OCR2-2B-Instruct",
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# torch_dtype=torch.bfloat16,
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# attn_implementation="flash_attention_2",
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# device_map="auto",
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# )
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# default processor
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processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-OCR2-2B-Instruct")
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# 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.
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# min_pixels = 256*28*28
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# max_pixels = 1280*28*28
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# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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}
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]
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# Preparation for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to("cuda")
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# Inference: Generation of the output
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generated_ids = model.generate(**inputs, max_new_tokens=128)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text)
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```
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### Buffering Output
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```python
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buffer = ""
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for new_text in streamer:
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buffer += new_text
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# Remove <|im_end|> or similar tokens from the output
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buffer = buffer.replace("<|im_end|>", "")
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yield buffer
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```
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### **Key Features**
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1. **Vision-Language Integration:**
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- Combines **image understanding** with **natural language processing** to convert images into text.
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2. **Optical Character Recognition (OCR):**
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- Extracts and processes textual information from images with high accuracy.
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3. **Math and LaTeX Support:**
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- Solves math problems and outputs equations in **LaTeX format**.
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4. **Conversational Capabilities:**
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- Designed to handle **multi-turn interactions**, providing context-aware responses.
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5. **Image-Text-to-Text Generation:**
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- Inputs can include **images, text, or a combination**, and the model generates descriptive or problem-solving text. |