Instructions to use Ostgot/blip-large-codenrock-solution with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ostgot/blip-large-codenrock-solution with Transformers:

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

pipe = pipeline("image-text-to-text", model="Ostgot/blip-large-codenrock-solution")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Ostgot/blip-large-codenrock-solution")
model = AutoModelForImageTextToText.from_pretrained("Ostgot/blip-large-codenrock-solution")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ostgot/blip-large-codenrock-solution with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Ostgot/blip-large-codenrock-solution

SGLang

How to use Ostgot/blip-large-codenrock-solution 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 "Ostgot/blip-large-codenrock-solution" \
    --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": "Ostgot/blip-large-codenrock-solution",
		"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 "Ostgot/blip-large-codenrock-solution" \
        --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": "Ostgot/blip-large-codenrock-solution",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ostgot/blip-large-codenrock-solution with Docker Model Runner:
```
docker model run hf.co/Ostgot/blip-large-codenrock-solution
```

Ostgot commited on Aug 24, 2023

Commit

4a72aca

1 Parent(s): 183166c

Upload BlipForConditionalGeneration

Browse files

Files changed (2) hide show

config.json +31 -0
pytorch_model.bin +3 -0

config.json ADDED Viewed

	@@ -0,0 +1,31 @@

+{
+  "_name_or_path": "Salesforce/blip-image-captioning-large",
+  "architectures": [
+    "BlipForConditionalGeneration"
+  ],
+  "image_text_hidden_size": 256,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "blip",
+  "projection_dim": 512,
+  "text_config": {
+    "encoder_hidden_size": 1024,
+    "initializer_factor": 1.0,
+    "model_type": "blip_text_model",
+    "num_attention_heads": 12
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.0.dev0",
+  "vision_config": {
+    "dropout": 0.0,
+    "hidden_size": 1024,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "model_type": "blip_vision_model",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 24
+  }
+}

pytorch_model.bin ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:fb7a2101222c6e686cf49c87f0e226f07a654fc2e600df3bf7a84b77c02a18eb
+size 1879148141