Instructions to use Evicka/HanseLM-78M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Evicka/HanseLM-78M-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Evicka/HanseLM-78M-Base", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Evicka/HanseLM-78M-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Evicka/HanseLM-78M-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Evicka/HanseLM-78M-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Evicka/HanseLM-78M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Evicka/HanseLM-78M-Base
- SGLang
How to use Evicka/HanseLM-78M-Base 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 "Evicka/HanseLM-78M-Base" \ --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": "Evicka/HanseLM-78M-Base", "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 "Evicka/HanseLM-78M-Base" \ --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": "Evicka/HanseLM-78M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Evicka/HanseLM-78M-Base with Docker Model Runner:
docker model run hf.co/Evicka/HanseLM-78M-Base
Training data sources and attribution
The model repository does not redistribute source documents or processed training datasets. The model was trained on sampled text from:
- FineWeb2,
configuration
deu_Latn, released under ODC-By 1.0. - FineWiki,
configuration
de, derived from German Wikipedia text and identified by its dataset card as CC BY-SA 4.0 and GFDL. - FineWeb,
configuration
default, released under ODC-By 1.0 and additionally subject to Common Crawl's Terms of Use.
FineWiki attributes its text to Wikipedia contributors. FineWeb and FineWeb2 should be cited according to their dataset cards. Users who reproduce, continue, or redistribute training data are responsible for complying with the source licenses and terms.
The repository's MIT license covers HanseLM's release code and model weights. It does not relicense third-party source datasets or grant rights to training examples that may be reproduced by the model.