Instructions to use Skywork/Skywork-13B-Base-3.1TB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 (500B–3.1T) — repo is 404, could you re-upload?

by TedHuang - opened 19 days ago

Discussion

TedHuang

19 days ago

Hi Skywork team,

Thanks for open-sourcing Skywork-13B and SkyPile!

The paper mentions intermediate checkpoints at 500B/1T/1.5T/2T/2.5T/3T/3.1T tokens, and discussion #4 confirmed they were uploaded to Skywork-13B-Base-Intermediate, but that repo now returns a 404 and I can't find them elsewhere.

Could you re-upload them or point me to the current location? A Pythia-style layout (one branch/revision per checkpoint) would be ideal, but any format works. Thank you!

Best,
Ted

OrlandoHugBot

Skywork org 12 days ago

https://huggingface.co/Skywork/Skywork-13B-base

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