Text Generation
Transformers
Safetensors
German
English
hanse
causal-lm
custom-code
research
custom_code
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](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2), | |
| configuration `deu_Latn`, released under ODC-By 1.0. | |
| - [FineWiki](https://huggingface.co/datasets/HuggingFaceFW/finewiki), | |
| configuration `de`, derived from German Wikipedia text and identified by its | |
| dataset card as CC BY-SA 4.0 and GFDL. | |
| - [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/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. | |