transformers safetensors qwen2_vl image-text-to-text text-generation-inference ocr vl 2B conversational en zh dataset:linxy/LaTeX_OCR dataset:unsloth/LaTeX_OCR dataset:v1v1d/Latexify_v1 dataset:lamm-mit/OleehyO-latex-formulas base_model:Qwen/Qwen2-VL-2B-Instruct base_model:finetune:Qwen/Qwen2-VL-2B-Instruct license:apache-2.0 endpoints_compatible region:us
---
base_model:
- Qwen/Qwen2-VL-2B-Instruct
datasets:
- linxy/LaTeX_OCR
- unsloth/LaTeX_OCR
- v1v1d/Latexify_v1
- lamm-mit/OleehyO-latex-formulas
language:
- en
- zh
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- text-generation-inference
- ocr
- vl
- qwen2_vl
- 2B
---
![aWERGSRFH.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/0kXlF1G9NdID1279w_yfw.png)

> previous version `https://huggingface.co/prithivMLmods/Qwen2-VL-OCR-2B-Instruct`

# **Qwen2-VL-OCR2-2B-Instruct**

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.

#### 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.

### 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-OCR2-2B-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-OCR2-2B-Instruct",
#     torch_dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

# default processor
processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-OCR2-2B-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)
```
### Buffering Output
```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.
