Text Generation
Transformers
PyTorch
JAX
Russian
gpt2
PyTorch
Transformers
text-generation-inference
Instructions to use DFofanov78/rugpt3large_based_on_gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DFofanov78/rugpt3large_based_on_gpt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DFofanov78/rugpt3large_based_on_gpt2")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("DFofanov78/rugpt3large_based_on_gpt2") model = AutoModelForMultimodalLM.from_pretrained("DFofanov78/rugpt3large_based_on_gpt2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DFofanov78/rugpt3large_based_on_gpt2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DFofanov78/rugpt3large_based_on_gpt2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DFofanov78/rugpt3large_based_on_gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DFofanov78/rugpt3large_based_on_gpt2
- SGLang
How to use DFofanov78/rugpt3large_based_on_gpt2 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 "DFofanov78/rugpt3large_based_on_gpt2" \ --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": "DFofanov78/rugpt3large_based_on_gpt2", "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 "DFofanov78/rugpt3large_based_on_gpt2" \ --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": "DFofanov78/rugpt3large_based_on_gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DFofanov78/rugpt3large_based_on_gpt2 with Docker Model Runner:
docker model run hf.co/DFofanov78/rugpt3large_based_on_gpt2
- Xet hash:
- 7e635ee8e66f5d7bdaad802858f00aa35466834f3feda8651196877c76fddf7a
- Size of remote file:
- 3.04 GB
- SHA256:
- 69f93f7c9af006b72f3e61038597127ff41cf2f5752b35db64fdca1a7b1242ad
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