Instructions to use kneelabh87/blip-fast-debug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kneelabh87/blip-fast-debug with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="kneelabh87/blip-fast-debug")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("kneelabh87/blip-fast-debug")
model = AutoModelForMultimodalLM.from_pretrained("kneelabh87/blip-fast-debug", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kneelabh87/blip-fast-debug with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kneelabh87/blip-fast-debug"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kneelabh87/blip-fast-debug",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kneelabh87/blip-fast-debug

SGLang

How to use kneelabh87/blip-fast-debug 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 "kneelabh87/blip-fast-debug" \
    --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": "kneelabh87/blip-fast-debug",
		"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 "kneelabh87/blip-fast-debug" \
        --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": "kneelabh87/blip-fast-debug",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kneelabh87/blip-fast-debug with Docker Model Runner:
```
docker model run hf.co/kneelabh87/blip-fast-debug
```

blip-fast-debug

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{
  "architectures": [
    "BlipForConditionalGeneration"
  ],
  "image_text_hidden_size": 256,
  "initializer_factor": 1.0,
  "initializer_range": 0.02,
  "label_smoothing": 0.0,
  "logit_scale_init_value": 2.6592,
  "model_type": "blip",
  "projection_dim": 512,
  "text_config": {
    "attention_probs_dropout_prob": 0.0,
    "encoder_hidden_size": 768,
    "hidden_act": "gelu",
    "hidden_dropout_prob": 0.0,
    "hidden_size": 768,
    "initializer_factor": 1.0,
    "initializer_range": 0.02,
    "intermediate_size": 3072,
    "label_smoothing": 0.0,
    "layer_norm_eps": 1e-12,
    "max_position_embeddings": 512,
    "model_type": "blip_text_model",
    "num_attention_heads": 12,
    "num_hidden_layers": 12,
    "projection_dim": 768,
    "torch_dtype": "float32",
    "use_cache": true,
    "vocab_size": 30524
  },
  "torch_dtype": "float32",
  "transformers_version": "4.55.1",
  "vision_config": {
    "attention_dropout": 0.0,
    "dropout": 0.0,
    "hidden_act": "gelu",
    "hidden_size": 768,
    "image_size": 384,
    "initializer_factor": 1.0,
    "initializer_range": 0.02,
    "intermediate_size": 3072,
    "layer_norm_eps": 1e-05,
    "model_type": "blip_vision_model",
    "num_attention_heads": 12,
    "num_channels": 3,
    "num_hidden_layers": 12,
    "patch_size": 16,
    "projection_dim": 512,
    "torch_dtype": "float32"
  }
}