Instructions to use mrm8488/dollcerberoom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/dollcerberoom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrm8488/dollcerberoom")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrm8488/dollcerberoom", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mrm8488/dollcerberoom with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mrm8488/dollcerberoom" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrm8488/dollcerberoom", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mrm8488/dollcerberoom
- SGLang
How to use mrm8488/dollcerberoom 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 "mrm8488/dollcerberoom" \ --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/dollcerberoom", "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 "mrm8488/dollcerberoom" \ --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/dollcerberoom", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mrm8488/dollcerberoom with Docker Model Runner:
docker model run hf.co/mrm8488/dollcerberoom
DOLLcerberOOM: 3 x Dolly ๐ + BLOOMz ๐ฎ
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, French and German by Argilla) by using the method LoRA.
Model Description
Instruction Tuned version of BigScience Large Open-science Open-access Multilingual.
Training data
This collection of datasets are machine-translated (and soon curated) versions of the databricks-dolly-15k dataset originally created by Databricks, Inc. in 2023.
The goal is to give practitioners a starting point for training open-source instruction-following models beyond English. However, as the translation quality will not be perfect, we highly recommend dedicating time to curate and fix translation issues. Below we explain how to load the datasets into Argilla for data curation and fixing. Additionally, we'll be improving the datasets made available here, with the help of different communities.
We highly recommend dataset curation beyond proof-of-concept experiments.
Supported Tasks and Leaderboards
TBA
Training procedure
TBA
How to use
TBA