Instructions to use microsoft/kosmos-2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/kosmos-2.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="microsoft/kosmos-2.5")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("microsoft/kosmos-2.5") model = AutoModelForImageTextToText.from_pretrained("microsoft/kosmos-2.5") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use microsoft/kosmos-2.5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/kosmos-2.5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/kosmos-2.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/microsoft/kosmos-2.5
- SGLang
How to use microsoft/kosmos-2.5 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 "microsoft/kosmos-2.5" \ --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": "microsoft/kosmos-2.5", "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 "microsoft/kosmos-2.5" \ --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": "microsoft/kosmos-2.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use microsoft/kosmos-2.5 with Docker Model Runner:
docker model run hf.co/microsoft/kosmos-2.5
Replace **dtype** with **torch_dtype** in Model Loading
The model card for Kosmos 2.5 has been updated to address an issue where the dtype parameter in the from_pretrained method was not functioning as intended. The code has been revised to use torch_dtype instead, aligning with the standard PyTorch API convention in Hugging Face's Transformers library. This change ensures proper data type specification during model loading and resolves issues with the previous dtype parameter.
The updated code is as follows:
model = Kosmos2_5ForConditionalGeneration.from_pretrained(repo, device_map=device, torch_dtype=dtype)
This update does not alter the model's functionality but ensures compatibility with the Hugging Face Transformers API.
Additionally, I have submitted a pull request to update the Kosmos 2.5 README file documentation in the Hugging Face Transformers library to reflect this change: https://github.com/huggingface/transformers/pull/40529