Instructions to use digitous/Alpacino30b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digitous/Alpacino30b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="digitous/Alpacino30b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("digitous/Alpacino30b") model = AutoModelForCausalLM.from_pretrained("digitous/Alpacino30b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use digitous/Alpacino30b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "digitous/Alpacino30b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "digitous/Alpacino30b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/digitous/Alpacino30b
- SGLang
How to use digitous/Alpacino30b 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 "digitous/Alpacino30b" \ --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": "digitous/Alpacino30b", "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 "digitous/Alpacino30b" \ --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": "digitous/Alpacino30b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use digitous/Alpacino30b with Docker Model Runner:
docker model run hf.co/digitous/Alpacino30b
Training Setup
Hi There!
I'm new to a lot of this, and have a very constrained schedule, so I haven't been able to dedicate the time I'd really like to start doing the level of training that interests me. So far, I've only been doing LoRA training on oobabooga, and I've gotten OOM right at the end of every training attempt through that method on 30B+ models.
Is there a particular script or framework you use for training, merging, etc on these larger models that allows for use of multiple GPUs? I figure I'm probably going to need to buckle down and read some books/papers to really understand what's going on, but things are moving so fast it's hard to know where I can best spend my time learning.
Either way, really appreciate the work you've done here!
-J
Thanks for asking and your interest!
Weird as it sounds, I don't train these models or the LoRAs used in them; I use a model merge script and a lora merge script to do the work that goes into these.
https://github.com/Digitous/Enhanced-LM-Mixer
https://github.com/tloen/alpaca-lora
Appreciate the answer anyway! Every link I get together gets me a bit closer to working in this ecosystem. Lots of fun to be had, I think!
Take care, and thanks again for the models and the engagement!
-J