Instructions to use vikhyatk/moondream1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikhyatk/moondream1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="vikhyatk/moondream1", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("vikhyatk/moondream1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use vikhyatk/moondream1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vikhyatk/moondream1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vikhyatk/moondream1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vikhyatk/moondream1
- SGLang
How to use vikhyatk/moondream1 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 "vikhyatk/moondream1" \ --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": "vikhyatk/moondream1", "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 "vikhyatk/moondream1" \ --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": "vikhyatk/moondream1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vikhyatk/moondream1 with Docker Model Runner:
docker model run hf.co/vikhyatk/moondream1
Troubleshooting Tensor Dimension Mismatch
Hello,
When I try to compile the example from the model card, I encounter a matrix error.
modeling_phi.py:
Code line 313: padding_mask.masked_fill_(key_padding_mask, 0.0)
RuntimeError: The size of tensor a (749) must match the size of tensor b (750) at non-singleton dimension 1.
The error indicates a tensor size mismatch during a computation in your model, specifically when tensors of size 749 and 750 are attempted to be processed together at a dimension where their sizes must match. Any ideas why this is happening? Thank you.
Operating system: Windows 11 Home
Operating system version: 10.0.22631
Python version: 3.12.2
PyTorch version: 2.2.1+cu121
Torchvision version: 2.2.1+cu121
CUDA version: 12.1
CUDNN version: 8801
Current CUDA device: 0
Number of CUDA devices available: 1
Name of current CUDA device: NVIDIA GeForce RTX 3070 Ti
Check if CUDA is available: True
There was a backward incompatible change to the KV cache introduced in the 4.38.0 release of transformers. Three options:
- Use moondream2, where the issue is fixed.
- Downgrade transformers to 4.37.2.
- Try the patch mentioned here.
