Instructions to use dad887/DAD-Public-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dad887/DAD-Public-Models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="dad887/DAD-Public-Models")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dad887/DAD-Public-Models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dad887/DAD-Public-Models with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dad887/DAD-Public-Models" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dad887/DAD-Public-Models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dad887/DAD-Public-Models
- SGLang
How to use dad887/DAD-Public-Models 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 "dad887/DAD-Public-Models" \ --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": "dad887/DAD-Public-Models", "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 "dad887/DAD-Public-Models" \ --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": "dad887/DAD-Public-Models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dad887/DAD-Public-Models with Docker Model Runner:
docker model run hf.co/dad887/DAD-Public-Models
Download NOTICE from dad887/DAD-Public-Models: direct link, hf CLI and curl.
- Browser
- Download file 648 Bytes
-
https://huggingface.co/dad887/DAD-Public-Models/resolve/main/NOTICE
- Command line
-
hf download hf://dad887/DAD-Public-Models/NOTICE
-
curl -L -o NOTICE https://huggingface.co/dad887/DAD-Public-Models/resolve/main/NOTICE
648 Bytes
| DAD public-data experiment models | |
| These fine-tuned models are based on Qwen3-VL-2B-Instruct by the Qwen Team: | |
| https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct | |
| The base model is licensed under the Apache License, Version 2.0. | |
| A copy is included at licenses/Apache-2.0.txt. | |
| The released variants incorporate supervised fine-tuning and, for GRPO and | |
| EleRPO, reinforcement-learning updates. Adapter weights have been merged into | |
| the base model to provide standalone inference checkpoints. | |
| The license for these fine-tuned releases is CC BY-NC 4.0, included in LICENSE. | |
| This does not replace the license of the original, unmodified Qwen base model. | |