Instructions to use Vrjb/BLIP_Captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vrjb/BLIP_Captioning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Vrjb/BLIP_Captioning")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Vrjb/BLIP_Captioning") model = AutoModelForImageTextToText.from_pretrained("Vrjb/BLIP_Captioning") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Vrjb/BLIP_Captioning with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vrjb/BLIP_Captioning" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vrjb/BLIP_Captioning", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Vrjb/BLIP_Captioning
- SGLang
How to use Vrjb/BLIP_Captioning with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Vrjb/BLIP_Captioning" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vrjb/BLIP_Captioning", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Vrjb/BLIP_Captioning" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vrjb/BLIP_Captioning", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Vrjb/BLIP_Captioning with Docker Model Runner:
docker model run hf.co/Vrjb/BLIP_Captioning
Model save
Browse files- README.md +4 -4
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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- Bleu: 1.0
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Bleu |
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### Framework versions
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This model is a fine-tuned version of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3617
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- Bleu: 1.0
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Bleu |
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| 1.3635 | 1.0 | 779 | 1.3968 | 1.0 |
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| 1.3618 | 2.0 | 1558 | 1.3618 | 1.0 |
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| 1.3617 | 3.0 | 2337 | 1.3617 | 1.0 |
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### Framework versions
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model.safetensors
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