Instructions to use Skywork/Skywork-13B-Base-3.1TB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skywork/Skywork-13B-Base-3.1TB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Skywork/Skywork-13B-Base-3.1TB", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Skywork/Skywork-13B-Base-3.1TB", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Skywork/Skywork-13B-Base-3.1TB with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Skywork/Skywork-13B-Base-3.1TB" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skywork/Skywork-13B-Base-3.1TB", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Skywork/Skywork-13B-Base-3.1TB
- SGLang
How to use Skywork/Skywork-13B-Base-3.1TB 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 "Skywork/Skywork-13B-Base-3.1TB" \ --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": "Skywork/Skywork-13B-Base-3.1TB", "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 "Skywork/Skywork-13B-Base-3.1TB" \ --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": "Skywork/Skywork-13B-Base-3.1TB", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Skywork/Skywork-13B-Base-3.1TB with Docker Model Runner:
docker model run hf.co/Skywork/Skywork-13B-Base-3.1TB
Intermediate Checkpoints
I understand it may be cumbersome, but has the team considered releasing more intermediate checkpoints? IF you have them on hand or are training another one, consider releasing checkpoints in closer intervals similar to Pythia.
Yes, it is entirely possible to release more intermediate checkpoints, e.g. 500B, 1T, 1.5T, 2T, 2.5T, 3T. Would that meet your needs?
That would be great! My proposal is that there is a new repository called "Skywork-13B-Base-Intermediate" where every checkpoint saved as a different branch in one repo. https://huggingface.co/EleutherAI/pythia-70m That would be a great effort to make a good alternative to Pythia. If it's saved and not critical to anything Huggingface storage is free anyways so might as well put all you have. Just my opinion. Thank you for responding!
That would be great! My proposal is that there is a new repository called "Skywork-13B-Base-Intermediate" where every checkpoint saved as a different branch in one repo. https://huggingface.co/EleutherAI/pythia-70m That would be a great effort to make a good alternative to Pythia. If it's saved and not critical to anything Huggingface storage is free anyways so might as well put all you have. Just my opinion. Thank you for responding!
Hi there, we have uploaded intermediate checkpoints in the following repo: https://huggingface.co/Skywork/Skywork-13B-Base-Intermediate.
Hope that helps!
I deeply appreciate the release of these intermediate checkpoints and I am excited to use them for research. Thank you for working with me and generally the community to make AI transparent and open as it always should be.