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prithivMLmods
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Model Details
Model Provider
prithivMLmods
Model Tree
Qwen/Qwen2-VL-2B-Instruct
Input Modalities
Output Modalities
Supported Functionality
The Qwen2-VL-OCR-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.
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 |
python
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom qwen_vl_utils import process_vision_info # default: Load the model on the available device(s)model = Qwen2VLForConditionalGeneration.from_pretrained( "prithivMLmods/Qwen2-VL-OCR-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-OCR-2B-Instruct",# torch_dtype=torch.bfloat16,# attn_implementation="flash_attention_2",# device_map="auto",# ) # default processerprocessor = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-OCR-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 inferencetext = 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 outputgenerated_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)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 bufferVision-Language Integration:
Optical Character Recognition (OCR):
Math and LaTeX Support:
Conversational Capabilities:
Image-Text-to-Text Generation:
Secure Weight Format:
Base Model: Qwen/Qwen2-VL-2B-Instruct
Model Size:
Specializations:
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom qwen_vl_utils import process_vision_info # default: Load the model on the available device(s)model = Qwen2VLForConditionalGeneration.from_pretrained( "prithivMLmods/Qwen2-VL-OCR-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-OCR-2B-Instruct",# torch_dtype=torch.bfloat16,# attn_implementation="flash_attention_2",# device_map="auto",# ) # default processerprocessor = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-OCR-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 inferencetext = 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 outputgenerated_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) 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