|
--- |
|
license: apache-2.0 |
|
language: |
|
- en |
|
base_model: |
|
- Qwen/Qwen2-VL-2B-Instruct |
|
pipeline_tag: image-text-to-text |
|
library_name: transformers |
|
tags: |
|
- Math |
|
- Language |
|
- VLM |
|
- Character-Anology |
|
- multimodal |
|
--- |
|
# Qwen2-VL-Ocrtest-2B-Instruct [Text Analogy Ocrtest] |
|
|
|
 |
|
|
|
The **Qwen2-VL-Ocrtest-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 | |
|
|
|
--- |
|
### 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-Ocrtest-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-Ocrtest-2B-Instruct", |
|
# torch_dtype=torch.bfloat16, |
|
# attn_implementation="flash_attention_2", |
|
# device_map="auto", |
|
# ) |
|
|
|
# default processer |
|
processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-Ocrtest-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) |
|
``` |
|
|
|
### **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. |
|
|
|
--- |
