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metadata
library_name: transformers
datasets:
  - Joctor/cn_bokete_oogiri_caption
base_model:
  - Qwen/Qwen2-VL-7B-Instruct
pipeline_tag: image-to-text

Model Card for Model ID

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Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Uses

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info

model_id = "Joctor/qwen2-vl-7b-instruct-ogiri"

default: Load the model on the available device(s)

model = Qwen2VLForConditionalGeneration.from_pretrained( model_id, torch_dtype="auto", device_map="auto" )

default processer

processor = AutoProcessor.from_pretrained(model_id)

messages = [ { "role": "user", "content": [ { "type": "image", "image": "path/to/image", }, {"type": "text", "text": "根据图片给出有趣巧妙的回答"}, ], } ]

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)

Training Details

Training Data

https://huggingface.co/datasets/Joctor/cn_bokete_oogiri_caption

Training Procedure

基础模型:qwen2vl 微调方式:数据量充足,采用SFT微调 微调参数:max_length=1024(短就是好!), num_train_epochs=1, per_device_train_batch_size=1, gradient_accumulation_steps=1 训练设备:10 * 4090D 训练时长:22小时

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

https://www.gcores.com/articles/188405

Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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