Text Generation
PEFT
Safetensors
Transformers
English
German
Italian
gemma4
lora
mosaic-kg
knowledge-graph
webmaster
structured-output
alpine
tourism
conversational
Instructions to use cyberandy/Alpino-e4b-v01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cyberandy/Alpino-e4b-v01 with PEFT:
Task type is invalid.
- Transformers
How to use cyberandy/Alpino-e4b-v01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cyberandy/Alpino-e4b-v01") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cyberandy/Alpino-e4b-v01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cyberandy/Alpino-e4b-v01 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cyberandy/Alpino-e4b-v01" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cyberandy/Alpino-e4b-v01", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cyberandy/Alpino-e4b-v01
- SGLang
How to use cyberandy/Alpino-e4b-v01 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 "cyberandy/Alpino-e4b-v01" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cyberandy/Alpino-e4b-v01", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "cyberandy/Alpino-e4b-v01" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cyberandy/Alpino-e4b-v01", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cyberandy/Alpino-e4b-v01 with Docker Model Runner:
docker model run hf.co/cyberandy/Alpino-e4b-v01
Download tokenizer.json from cyberandy/Alpino-e4b-v01: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/cyberandy/Alpino-e4b-v01/resolve/main/tokenizer.json
- Command line
-
hf download hf://cyberandy/Alpino-e4b-v01/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/cyberandy/Alpino-e4b-v01/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- e96a17bb638d7e5c417d4c2abf3cdb5af7a97b50605a250f2f6d84c29b46d1d9
- Size of remote file:
- 32.2 MB
- SHA256:
- a2619fe11b50dbed06ac443c51d757b354d0b62d64baa514404d4e84e6713519
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