Instructions to use inkoziev/rugpt_chitchat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inkoziev/rugpt_chitchat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inkoziev/rugpt_chitchat")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("inkoziev/rugpt_chitchat") model = AutoModelForCausalLM.from_pretrained("inkoziev/rugpt_chitchat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use inkoziev/rugpt_chitchat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inkoziev/rugpt_chitchat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inkoziev/rugpt_chitchat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/inkoziev/rugpt_chitchat
- SGLang
How to use inkoziev/rugpt_chitchat 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 "inkoziev/rugpt_chitchat" \ --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": "inkoziev/rugpt_chitchat", "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 "inkoziev/rugpt_chitchat" \ --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": "inkoziev/rugpt_chitchat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use inkoziev/rugpt_chitchat with Docker Model Runner:
docker model run hf.co/inkoziev/rugpt_chitchat
Download pytorch_model.bin from inkoziev/rugpt_chitchat: direct link, hf CLI and curl.
- Browser
- Download file 3.14 GB
-
https://huggingface.co/inkoziev/rugpt_chitchat/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://inkoziev/rugpt_chitchat/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/inkoziev/rugpt_chitchat/resolve/main/pytorch_model.bin
3.14 GB
- Xet hash:
- 8234a29a66e1fc2bf4b754e43751db77ed0fb3b86365db688c59ac878f63e61f
- Size of remote file:
- 3.14 GB
- SHA256:
- 740931d5ad2fb88b5aaf466ee9cbd2702a38601261e61a6918e3b2a5369c5bb8
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