How to use from
SGLangUse 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 "mrm8488/dolloom" \
--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": "mrm8488/dolloom",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
DOLLOOM: Dolly 🐑 + BLOOMz 💮
Adapter Description
This adapter was created with the PEFT library and allowed the base model BigScience/BLOOMz 7B1 to be fine-tuned on the Dolly's Dataset (tanslated to Spanish) by using the method LoRA.
Model Description
Instruction Tuned version of BigScience Large Open-science Open-access Multilingual.
Training data
TBA
Supported Tasks and Leaderboards
TBA
Training procedure
TBA
How to use
TBA
Citation
@misc {manuel_romero_2023,
author = { {Manuel Romero} },
title = { dolloom (Revision 599b95a) },
year = 2023,
url = { https://huggingface.co/mrm8488/dolloom },
doi = { 10.57967/hf/0540 },
publisher = { Hugging Face }
}
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "mrm8488/dolloom" \ --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": "mrm8488/dolloom", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'