Text Generation
Transformers
PyTorch
TensorFlow
JAX
Core ML
ONNX
Safetensors
Hebrew
gpt2
text-generation-inference
Instructions to use Norod78/distilgpt2-base-pretrained-he with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Norod78/distilgpt2-base-pretrained-he with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Norod78/distilgpt2-base-pretrained-he")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Norod78/distilgpt2-base-pretrained-he") model = AutoModelForCausalLM.from_pretrained("Norod78/distilgpt2-base-pretrained-he") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Norod78/distilgpt2-base-pretrained-he with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Norod78/distilgpt2-base-pretrained-he" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Norod78/distilgpt2-base-pretrained-he", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Norod78/distilgpt2-base-pretrained-he
- SGLang
How to use Norod78/distilgpt2-base-pretrained-he 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 "Norod78/distilgpt2-base-pretrained-he" \ --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": "Norod78/distilgpt2-base-pretrained-he", "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 "Norod78/distilgpt2-base-pretrained-he" \ --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": "Norod78/distilgpt2-base-pretrained-he", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Norod78/distilgpt2-base-pretrained-he with Docker Model Runner:
docker model run hf.co/Norod78/distilgpt2-base-pretrained-he
Doron Adler
Further train for another 1250K steps (since base) on CC-100, Twitter, Updated Wikipedia
b8d558e - Xet hash:
- 07a5f4e722be9ac1be0565b540a7bb7344f702a152842af8f104a7a9ac662adb
- Size of remote file:
- 3.44 kB
- SHA256:
- 8e125ee34b8024c8d459873f8d086614ae564cf87b4eb1946507fe379c695479
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