Instructions to use readerbench/RoGPT2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use readerbench/RoGPT2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="readerbench/RoGPT2-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("readerbench/RoGPT2-base") model = AutoModelForCausalLM.from_pretrained("readerbench/RoGPT2-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use readerbench/RoGPT2-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "readerbench/RoGPT2-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "readerbench/RoGPT2-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/readerbench/RoGPT2-base
- SGLang
How to use readerbench/RoGPT2-base 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 "readerbench/RoGPT2-base" \ --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": "readerbench/RoGPT2-base", "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 "readerbench/RoGPT2-base" \ --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": "readerbench/RoGPT2-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use readerbench/RoGPT2-base with Docker Model Runner:
docker model run hf.co/readerbench/RoGPT2-base
ReaderBench commited on
Commit ·
cd10edc
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Parent(s): 0743a18
update how to use
Browse files
README.md
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```python
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# TensorFlow
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from transformers import AutoTokenizer,
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tokenizer = AutoTokenizer.from_pretrained('readerbench/RoGPT2-base')
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model =
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inputs = tokenizer.encode("Este o zi de vara", return_tensors='tf')
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text = model.generate(inputs, max_length=1024, no_repeat_ngram_size=2)
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print(tokenizer.decode(text[0]))
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# PyTorch
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from transformers import AutoTokenizer,
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tokenizer = AutoTokenizer.from_pretrained('readerbench/RoGPT2-base')
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model =
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inputs = tokenizer.encode("Este o zi de vara", return_tensors='pt')
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text = model.generate(inputs, max_length=1024, no_repeat_ngram_size=2)
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print(tokenizer.decode(text[0]))
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```python
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# TensorFlow
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from transformers import AutoTokenizer, TFAutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained('readerbench/RoGPT2-base')
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model = TFAutoModelForCausalLM.from_pretrained('readerbench/RoGPT2-base')
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inputs = tokenizer.encode("Este o zi de vara", return_tensors='tf')
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text = model.generate(inputs, max_length=1024, no_repeat_ngram_size=2)
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print(tokenizer.decode(text[0]))
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# PyTorch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained('readerbench/RoGPT2-base')
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model = AutoModelForCausalLM.from_pretrained('readerbench/RoGPT2-base')
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inputs = tokenizer.encode("Este o zi de vara", return_tensors='pt')
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text = model.generate(inputs, max_length=1024, no_repeat_ngram_size=2)
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print(tokenizer.decode(text[0]))
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