Instructions to use PracticeLLM/Twice-KoSOLAR-16.1B-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PracticeLLM/Twice-KoSOLAR-16.1B-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PracticeLLM/Twice-KoSOLAR-16.1B-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("PracticeLLM/Twice-KoSOLAR-16.1B-test") model = AutoModelForCausalLM.from_pretrained("PracticeLLM/Twice-KoSOLAR-16.1B-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PracticeLLM/Twice-KoSOLAR-16.1B-test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PracticeLLM/Twice-KoSOLAR-16.1B-test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PracticeLLM/Twice-KoSOLAR-16.1B-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PracticeLLM/Twice-KoSOLAR-16.1B-test
- SGLang
How to use PracticeLLM/Twice-KoSOLAR-16.1B-test 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 "PracticeLLM/Twice-KoSOLAR-16.1B-test" \ --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": "PracticeLLM/Twice-KoSOLAR-16.1B-test", "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 "PracticeLLM/Twice-KoSOLAR-16.1B-test" \ --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": "PracticeLLM/Twice-KoSOLAR-16.1B-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PracticeLLM/Twice-KoSOLAR-16.1B-test with Docker Model Runner:
docker model run hf.co/PracticeLLM/Twice-KoSOLAR-16.1B-test
Commit History
-
Upload README.md
c5e81d4
-
Upload README.md
af443a1
-
Upload README.md
bb2523b -
Upload README.md
357fade -
Upload README.md
8115a4c -
Upload README.md
b89d6a4 -
Upload README.md
1f197e5 -
Upload README.md
df64bea -
Delete model-00005-of-00005.safetensors
e8aa4d7 -
Delete model-00003-of-00005.safetensors
d8375ba -
Delete model-00002-of-00005.safetensors
092fb43 -
Delete model-00001-of-00005.safetensors
c793054 -
Delete model-00004-of-00005.safetensors
0d13e68 -
Upload LlamaForCausalLM
722e372 -
Upload solar.png
997d79d -
Upload tokenizer
1373c7d -
Upload LlamaForCausalLM
2180417 -
initial commit
b2e33e1