Instructions to use openbmb/RLHF-V with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/RLHF-V with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openbmb/RLHF-V")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("openbmb/RLHF-V", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use openbmb/RLHF-V with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openbmb/RLHF-V" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/RLHF-V", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/openbmb/RLHF-V
- SGLang
How to use openbmb/RLHF-V 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 "openbmb/RLHF-V" \ --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": "openbmb/RLHF-V", "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 "openbmb/RLHF-V" \ --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": "openbmb/RLHF-V", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use openbmb/RLHF-V with Docker Model Runner:
docker model run hf.co/openbmb/RLHF-V
Download pytorch_model-00006-of-00006.bin from openbmb/RLHF-V: direct link, hf CLI and curl.
- Browser
- Download file 4.94 GB
-
https://huggingface.co/openbmb/RLHF-V/resolve/main/pytorch_model-00006-of-00006.bin
- Command line
-
hf download hf://openbmb/RLHF-V/pytorch_model-00006-of-00006.bin
-
curl -L -o pytorch_model-00006-of-00006.bin https://huggingface.co/openbmb/RLHF-V/resolve/main/pytorch_model-00006-of-00006.bin
4.94 GB
- Xet hash:
- c144108107a8c3cb42621439f3b4a95e2fcccd3b41f6a279221c4d75a06a3529
- Size of remote file:
- 4.94 GB
- SHA256:
- 319486cddba0556a18262acacce3319bbd73c9893e73e3d491be96f737617ff8
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