Instructions to use utischoolnlp/Polyverse-1.3B-256-16-stage1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use utischoolnlp/Polyverse-1.3B-256-16-stage1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="utischoolnlp/Polyverse-1.3B-256-16-stage1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("utischoolnlp/Polyverse-1.3B-256-16-stage1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use utischoolnlp/Polyverse-1.3B-256-16-stage1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "utischoolnlp/Polyverse-1.3B-256-16-stage1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "utischoolnlp/Polyverse-1.3B-256-16-stage1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/utischoolnlp/Polyverse-1.3B-256-16-stage1
- SGLang
How to use utischoolnlp/Polyverse-1.3B-256-16-stage1 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 "utischoolnlp/Polyverse-1.3B-256-16-stage1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "utischoolnlp/Polyverse-1.3B-256-16-stage1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "utischoolnlp/Polyverse-1.3B-256-16-stage1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "utischoolnlp/Polyverse-1.3B-256-16-stage1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use utischoolnlp/Polyverse-1.3B-256-16-stage1 with Docker Model Runner:
docker model run hf.co/utischoolnlp/Polyverse-1.3B-256-16-stage1
Download preprocessor_config.json from utischoolnlp/Polyverse-1.3B-256-16-stage1: direct link, hf CLI and curl.
- Browser
- Download file 397 Bytes
-
https://huggingface.co/utischoolnlp/Polyverse-1.3B-256-16-stage1/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://utischoolnlp/Polyverse-1.3B-256-16-stage1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/utischoolnlp/Polyverse-1.3B-256-16-stage1/resolve/main/preprocessor_config.json
397 Bytes
| { | |
| "do_convert_rgb": null, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "SiglipImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "PolyverseProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 256, | |
| "width": 256 | |
| } | |
| } | |