pankajmathur/alpaca_orca
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How to use TitleOS/Seahorse-350m with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="TitleOS/Seahorse-350m") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TitleOS/Seahorse-350m")
model = AutoModelForCausalLM.from_pretrained("TitleOS/Seahorse-350m")How to use TitleOS/Seahorse-350m with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TitleOS/Seahorse-350m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "TitleOS/Seahorse-350m",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/TitleOS/Seahorse-350m
How to use TitleOS/Seahorse-350m with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "TitleOS/Seahorse-350m" \
--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": "TitleOS/Seahorse-350m",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "TitleOS/Seahorse-350m" \
--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": "TitleOS/Seahorse-350m",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use TitleOS/Seahorse-350m with Docker Model Runner:
docker model run hf.co/TitleOS/Seahorse-350m
This is the first generation of a OPT based model, finetuned on the Orca dataset formatted to the Alpaca style.
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='TitleOS/Seahorse-350m')
>>> generator("Tell me about Alpacas.", do_sample=True, min_length=50)
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).
OPT-350M is licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}