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---
base_model: Salesforce/blip2-opt-2.7b
library_name: peft
---
# Model Card for Model ID
This is an adapter for the Salesforce BLIP2 2.7B model (more information on the model [here](https://huggingface.co/Salesforce/blip2-opt-2.7b)). It was fine-tuned for generating product descriptions based on images using the [H&M dataset](https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations) from the kaggle challenge 2022.
## How to Get Started with the Model
Use the code below to get started with the model. Make sure to replace the path with a local path to an image.
```python
from transformers import Blip2Processor, Blip2ForConditionalGeneration
from PIL import Image
import torch
torch_dtype = torch.bfloat16
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
base_checkpoint = "Salesforce/blip2-opt-2.7b"
base_model = Blip2ForConditionalGeneration.from_pretrained(base_checkpoint, torch_dtype=torch_dtype)
adapter_checkpoint = "CDL-RecSys/blip2-opt-2.7b-hm"
model = PeftModel.from_pretrained(base_model, model_id=adapter_checkpoint)
processor = Blip2Processor.from_pretrained(base_checkpoint)
tokenizer = processor.tokenizer
image = Image.open("path/to/your/image.jpg")
inputs = processor(image, return_tensors="pt").to(device, torch_dtype)
generated_ids = model.generate(**inputs, max_length=max_length)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
generated_text
```
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **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]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.12.0 |