Instructions to use 0x7o/rugpt3medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 0x7o/rugpt3medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="0x7o/rugpt3medium")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("0x7o/rugpt3medium") model = AutoModelForCausalLM.from_pretrained("0x7o/rugpt3medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 0x7o/rugpt3medium with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "0x7o/rugpt3medium" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "0x7o/rugpt3medium", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/0x7o/rugpt3medium
- SGLang
How to use 0x7o/rugpt3medium 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 "0x7o/rugpt3medium" \ --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": "0x7o/rugpt3medium", "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 "0x7o/rugpt3medium" \ --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": "0x7o/rugpt3medium", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 0x7o/rugpt3medium with Docker Model Runner:
docker model run hf.co/0x7o/rugpt3medium
Download pytorch_model.bin from 0x7o/rugpt3medium: direct link, hf CLI and curl.
- Browser
- Download file 135 Bytes
-
https://huggingface.co/0x7o/rugpt3medium/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://0x7o/rugpt3medium/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/0x7o/rugpt3medium/resolve/main/pytorch_model.bin
135 Bytes
- Xet hash:
- b5d3b18db490f5c6bff23be79389df111dd5f6c3ef5398a9f30f3508e23cef42
- Size of remote file:
- 135 Bytes
- SHA256:
- fdcf136033114f970947b4a6fdd2d53bb149f12a1ca0e36ac42468f5caac49f9
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