Instructions to use e-tornike/gpt2-rnm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use e-tornike/gpt2-rnm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="e-tornike/gpt2-rnm")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("e-tornike/gpt2-rnm") model = AutoModelForCausalLM.from_pretrained("e-tornike/gpt2-rnm") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use e-tornike/gpt2-rnm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "e-tornike/gpt2-rnm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "e-tornike/gpt2-rnm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/e-tornike/gpt2-rnm
- SGLang
How to use e-tornike/gpt2-rnm 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 "e-tornike/gpt2-rnm" \ --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": "e-tornike/gpt2-rnm", "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 "e-tornike/gpt2-rnm" \ --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": "e-tornike/gpt2-rnm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use e-tornike/gpt2-rnm with Docker Model Runner:
docker model run hf.co/e-tornike/gpt2-rnm
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Check out the documentation for more information.
How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='e-tony/gpt2-rnm')
>>> set_seed(42)
>>> generator("Rick: I turned myself into a pickle, Morty!\nMorty: ", max_length=50, num_return_sequences=5)
[{'generated_text': "Rick: I turned myself into a pickle, Morty!\nMorty: I didn't want to have children. It was my fate! I'll pay my mom and dad.\nSnuffles: Well, at least we"},
{'generated_text': "Rick: I turned myself into a pickle, Morty!\nMorty: you know what happened?\n(Steven begins dragging people down the toilet with his hand. As Steven falls) The whole thing starts.\nA man approaches Steven"},
{'generated_text': "Rick: I turned myself into a pickle, Morty!\nMorty: Oh wait! And do you remember what I did to you?\nJerry: Uh, it didn't hurt. It should have hurt a lot since I"},
{'generated_text': "Rick: I turned myself into a pickle, Morty!\nMorty: Rick!\nKraven: Wait! [wary gasp] What the hell are you doing this time?!\nJerry: Hey, are you"},
{'generated_text': "Rick: I turned myself into a pickle, Morty!\nMorty: Uh.\nJerry: You don't have to put your finger on me today, do you?\nRick: It's just, what do you"}]
Training data
We used the original gpt2 model and fine-tuned it on Rick and Morty transcripts.
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