Instructions to use vikhyatk/moondream2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikhyatk/moondream2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="vikhyatk/moondream2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("vikhyatk/moondream2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use vikhyatk/moondream2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vikhyatk/moondream2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vikhyatk/moondream2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vikhyatk/moondream2
- SGLang
How to use vikhyatk/moondream2 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/moondream2" \ --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/moondream2", "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/moondream2" \ --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/moondream2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vikhyatk/moondream2 with Docker Model Runner:
docker model run hf.co/vikhyatk/moondream2
Update README.md
Browse files
README.md
CHANGED
|
@@ -53,7 +53,12 @@ print(f"Found {len(points)} person(s)")
|
|
| 53 |
|
| 54 |
**2025-04-15**
|
| 55 |
|
| 56 |
-
(
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 57 |
|
| 58 |
**2025-03-27**
|
| 59 |
|
|
|
|
| 53 |
|
| 54 |
**2025-04-15**
|
| 55 |
|
| 56 |
+
1. Improved chart understanding (ChartQA up from 74.8 to 77.5, 82.2 with PoT)
|
| 57 |
+
2. Added temperature and nucleus sampling to reduce repetitive outputs
|
| 58 |
+
3. Better OCR for documents and tables (prompt with “Transcribe the text” or “Transcribe the text in natural reading order”)
|
| 59 |
+
4. Object detection supports document layout detection (figure, formula, text, etc)
|
| 60 |
+
5. UI understanding (ScreenSpot F1@0.5 up from 53.3 to 60.3)
|
| 61 |
+
6. Improved text understanding (DocVQA up from 76.5 to 79.3, TextVQA up from 74.6 to 76.3)
|
| 62 |
|
| 63 |
**2025-03-27**
|
| 64 |
|