Instructions to use BigSalmon/MrLincoln13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigSalmon/MrLincoln13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BigSalmon/MrLincoln13")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MrLincoln13") model = AutoModelForCausalLM.from_pretrained("BigSalmon/MrLincoln13") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use BigSalmon/MrLincoln13 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BigSalmon/MrLincoln13" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BigSalmon/MrLincoln13", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BigSalmon/MrLincoln13
- SGLang
How to use BigSalmon/MrLincoln13 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 "BigSalmon/MrLincoln13" \ --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": "BigSalmon/MrLincoln13", "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 "BigSalmon/MrLincoln13" \ --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": "BigSalmon/MrLincoln13", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BigSalmon/MrLincoln13 with Docker Model Runner:
docker model run hf.co/BigSalmon/MrLincoln13
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Check out the documentation for more information.
Informal to Formal:
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln13")
https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time)
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
informal english: meteors are much harder to see, because they are only there for a fraction of a second.
Translated into the Style of Abraham Lincoln: meteors are not ( easily / readily ) detectable, lasting for mere fractions of a second.
informal english:
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