Update README.md

README.md

@@ -12,69 +12,11 @@ pipeline_tag: image-to-text
 <!-- Provide a quick summary of what the model is/does. -->
 AI大喜利
 
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-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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-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
+```python
 from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
 from qwen_vl_utils import process_vision_info
 
@@ -124,6 +66,7 @@ output_text = processor.batch_decode(
     generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
 )
 print(output_text)
+```
 
 ## Training Details
 
@@ -137,121 +80,13 @@ https://huggingface.co/datasets/Joctor/cn_bokete_oogiri_caption
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
+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  
 
-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
 
 ### Results
 
-https://www.gcores.com/articles/188405
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
+https://www.gcores.com/articles/188405