Instructions to use seduerr/pai_infi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seduerr/pai_infi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="seduerr/pai_infi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("seduerr/pai_infi") model = AutoModelForCausalLM.from_pretrained("seduerr/pai_infi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use seduerr/pai_infi with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "seduerr/pai_infi" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "seduerr/pai_infi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/seduerr/pai_infi
- SGLang
How to use seduerr/pai_infi 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 "seduerr/pai_infi" \ --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": "seduerr/pai_infi", "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 "seduerr/pai_infi" \ --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": "seduerr/pai_infi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use seduerr/pai_infi with Docker Model Runner:
docker model run hf.co/seduerr/pai_infi
Download flax_model.msgpack from seduerr/pai_infi: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/seduerr/pai_infi/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://seduerr/pai_infi/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/seduerr/pai_infi/resolve/main/flax_model.msgpack
498 MB
- Xet hash:
- a323ed5fea5b4de680aaf190e141010d98d127856e432ba49932081a9d2f9661
- Size of remote file:
- 498 MB
- SHA256:
- 30567e99b1366aace20ffdc61af9c7f481d4578700542540e184aa5728959c23
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