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Model: prithivMLmods/Qwen2-VL-OCR2-2B-Instruct Source: Original Platform
2026-06-01 16:54:16 +08:00
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+---
+license: apache-2.0
+pipeline_tag: image-text-to-text
+datasets:
+- linxy/LaTeX_OCR
+- unsloth/LaTeX_OCR
+- v1v1d/Latexify_v1
+- lamm-mit/OleehyO-latex-formulas
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen2-VL-2B-Instruct
+library_name: transformers
+tags:
+- text-generation-inference
+- ocr
+- vl
+- qwen2_vl
+- 2B
+- VQA
+- KIE
+- Latex
+---
+# **Qwen2-VL-OCR2-2B-Instruct [ VL / OCR ]**
+
+![OCR2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/o6jr8Vl_Spy9IAxutAB3j.png)
+
+# **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.
+
+> [!note]
+> previous version https://huggingface.co/prithivMLmods/Qwen2-VL-OCR-2B-Instruct
+
+#### 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.