Instructions to use SkunkworksAI/BakLLaVA_v1_pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkunkworksAI/BakLLaVA_v1_pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SkunkworksAI/BakLLaVA_v1_pretrained")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SkunkworksAI/BakLLaVA_v1_pretrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SkunkworksAI/BakLLaVA_v1_pretrained with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SkunkworksAI/BakLLaVA_v1_pretrained" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SkunkworksAI/BakLLaVA_v1_pretrained", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SkunkworksAI/BakLLaVA_v1_pretrained
- SGLang
How to use SkunkworksAI/BakLLaVA_v1_pretrained 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 "SkunkworksAI/BakLLaVA_v1_pretrained" \ --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": "SkunkworksAI/BakLLaVA_v1_pretrained", "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 "SkunkworksAI/BakLLaVA_v1_pretrained" \ --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": "SkunkworksAI/BakLLaVA_v1_pretrained", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SkunkworksAI/BakLLaVA_v1_pretrained with Docker Model Runner:
docker model run hf.co/SkunkworksAI/BakLLaVA_v1_pretrained
Download mm_projector.bin from SkunkworksAI/BakLLaVA_v1_pretrained: direct link, hf CLI and curl.
- Browser
- Download file 8.4 MB
-
https://huggingface.co/SkunkworksAI/BakLLaVA_v1_pretrained/resolve/main/mm_projector.bin
- Command line
-
hf download hf://SkunkworksAI/BakLLaVA_v1_pretrained/mm_projector.bin
-
curl -L -o mm_projector.bin https://huggingface.co/SkunkworksAI/BakLLaVA_v1_pretrained/resolve/main/mm_projector.bin
8.4 MB
- Xet hash:
- 5557b4ac8dbe92f0cd5664f0ce6ca37c04ecea5fdfa72683961b2d8736493ba5
- Size of remote file:
- 8.4 MB
- SHA256:
- ae2e1d603a3de7a9ce61e0b2bfff10b9b222c623bfded22a2ac2ea38f5c04a1e
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