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Model: prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen2-VL-2B-Instruct
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pipeline_tag: image-text-to-text
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library_name: transformers
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---
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# Qwen2-VL-Math-Prase-2B-Instruct [ Math EQU]
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The **Qwen2-VL-Math-Prase-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.
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#### Key Enhancements:
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* **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.
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* **Understanding videos of 20min+**: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.
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* **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.
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* **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.
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| **File Name** | **Size** | **Description** | **Upload Status** |
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|---------------------------|------------|------------------------------------------------|-------------------|
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| `.gitattributes` | 1.52 kB | Configures LFS tracking for specific model files. | Initial commit |
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| `README.md` | 203 Bytes | Minimal details about the uploaded model. | Updated |
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| `added_tokens.json` | 408 Bytes | Additional tokens used by the model tokenizer. | Uploaded |
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| `chat_template.json` | 1.05 kB | Template for chat-based model input/output. | Uploaded |
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| `config.json` | 1.24 kB | Model configuration metadata. | Uploaded |
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| `generation_config.json` | 252 Bytes | Configuration for text generation settings. | Uploaded |
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| `merges.txt` | 1.82 MB | BPE merge rules for tokenization. | Uploaded |
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| `model.safetensors` | 4.42 GB | Serialized model weights in a secure format. | Uploaded (LFS) |
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| `preprocessor_config.json`| 596 Bytes | Preprocessing configuration for input data. | Uploaded |
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| `vocab.json` | 2.78 MB | Vocabulary file for tokenization. | Uploaded |
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---
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### How to Use
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```python
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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# default: Load the model on the available device(s)
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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"prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct", torch_dtype="auto", device_map="auto"
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)
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# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
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# model = Qwen2VLForConditionalGeneration.from_pretrained(
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# "prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct",
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# torch_dtype=torch.bfloat16,
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# attn_implementation="flash_attention_2",
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# device_map="auto",
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# )
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# default processer
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processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct")
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# 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.
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# min_pixels = 256*28*28
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# max_pixels = 1280*28*28
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# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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}
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]
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# Preparation for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to("cuda")
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# Inference: Generation of the output
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generated_ids = model.generate(**inputs, max_new_tokens=128)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text)
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```
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### **Key Features**
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1. **Vision-Language Integration:**
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- Combines **image understanding** with **natural language processing** to convert images into text.
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2. **Optical Character Recognition (OCR):**
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- Extracts and processes textual information from images with high accuracy.
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3. **Math and LaTeX Support:**
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- Solves math problems and outputs equations in **LaTeX format**.
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4. **Conversational Capabilities:**
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- Designed to handle **multi-turn interactions**, providing context-aware responses.
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5. **Image-Text-to-Text Generation:**
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- Inputs can include **images, text, or a combination**, and the model generates descriptive or problem-solving text.
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6. **Secure Weight Format:**
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- Uses **Safetensors** for faster and more secure model weight loading.
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---
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### **Training Details**
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- **Base Model:** [Qwen/Qwen2-VL-2B-Instruct](#)
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- **Model Size:**
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- 2.21 Billion parameters
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- Optimized for **BF16** tensor type, enabling efficient inference.
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- **Specializations:**
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- OCR tasks in images containing text.
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- Mathematical reasoning and LaTeX output for equations.
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---
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added_tokens.json
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{
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.json
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chat_template.json
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{
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"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
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}
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config.json
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config.json
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{
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"_name_or_path": "Qwen/Qwen2-VL-2B-Instruct",
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"architectures": [
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"Qwen2VLForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"image_token_id": 151655,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2_vl",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"pad_token_id": 151654,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"mrope_section": [
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16,
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24
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],
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"rope_type": "default",
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"type": "default"
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},
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"rope_theta": 1000000.0,
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"sliding_window": 32768,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.3",
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"unsloth_fixed": true,
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"use_cache": true,
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"use_sliding_window": false,
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"vision_config": {
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"hidden_size": 1536,
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"in_chans": 3,
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"model_type": "qwen2_vl",
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"spatial_patch_size": 14
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},
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"vision_start_token_id": 151652,
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"vision_token_id": 151654,
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"vocab_size": 151936
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}
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configuration.json
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configuration.json
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{"framework": "pytorch", "task": "image-text-to-text", "allow_remote": true}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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"max_length": 32768,
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"temperature": 0.01,
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"top_k": 1,
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"top_p": 0.001,
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"transformers_version": "4.46.3"
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}
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151388
merges.txt
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merges.txt
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ad92dc95b7e23e69a5d0d6b95cf343f1a587cfb257829586f74cd5582c167fbe
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size 4418050848
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preprocessor_config.json
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{
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.48145466,
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0.4578275,
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0.40821073
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],
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"image_processor_type": "Qwen2VLImageProcessor",
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"image_std": [
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"merge_size": 2,
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"min_pixels": 3136,
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"patch_size": 14,
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"processor_class": "Qwen2VLProcessor",
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"longest_edge": 12845056,
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"shortest_edge": 3136
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"temporal_patch_size": 2
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}
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vocab.json
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