---
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.
---