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)# pip install -U transformers accelerate # 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
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Download evaluation/README.md from Evicka/HanseLM-78M-Base: direct link, hf CLI and curl.
- Browser
- Download file 2.46 kB
-
https://huggingface.co/Evicka/HanseLM-78M-Base/resolve/main/evaluation/README.md
- Command line
-
hf download hf://Evicka/HanseLM-78M-Base/evaluation/README.md
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curl -L -o README.md https://huggingface.co/Evicka/HanseLM-78M-Base/resolve/main/evaluation/README.md
2.46 kB
| # Evaluation notes | |
| ## Held-out loss | |
| `internal-eval.json` records deterministic next-token loss measurements from | |
| the three source-specific validation files and the mixed validation file. | |
| These are corpus diagnostics, not standardized capability benchmarks. | |
| ## Generation and context checks | |
| `behavior-checks.json` contains four fixed-seed, 48-token samples at | |
| temperature 0.8 and top-k 50, plus an exact 2,048-token forward-pass smoke | |
| test. | |
| Observed in this small sample: | |
| - All four completions were non-empty and no German or English sample repeated | |
| a token trigram. | |
| - The deliberately mixed prompt continued in English and had a 0.0217 repeated | |
| trigram fraction. | |
| - German prompts stayed predominantly German and the English prompt stayed | |
| English, but this is manual inspection of four examples, not a language-ID | |
| benchmark. | |
| - Text was locally fluent in places but semantically unreliable. For example, | |
| the Hamburg continuation incorrectly referred to northern England and the | |
| Rhine. This supports the model card's strong factuality warning. | |
| - A forward pass at the trained limit of 2,048 tokens returned finite logits | |
| with shape `[1, 2048, 24576]`. | |
| The context smoke test proves implementation compatibility at 2,048 tokens, | |
| not long-context retrieval or reasoning. A later benchmark suite should add | |
| German and English likelihood tasks, multiple generation seeds, automatic | |
| language identification, repetition curves, and retrieval tests. | |
| ## Broader generation suite | |
| `generation-suite.json` contains 20 raw samples: ten German, English, mixed, | |
| factual, recipe, story, and repetition-oriented prompts evaluated with two | |
| seeds. The mean repeated token-trigram fraction was 0.0445; the maximum was | |
| 0.1613 and 15 of 20 samples had at least one repeated trigram. | |
| Manual review found locally plausible German and English syntax, but severe | |
| semantic and factual failures. Berlin was incorrectly continued as the | |
| capital of southern France or Croatia; German federal history was assigned | |
| incorrect dates and events; and water was said to freeze below two degrees or | |
| at up to 40 degrees. Recipes and stories had recognizable surface structure | |
| but commonly lost coherence. The mixed German/English prompt continued in | |
| German in both seeds. | |
| This result is appropriate for a tiny base-model research checkpoint: it has | |
| learned recognizable continuation patterns, but it is not a reliable factual | |
| model and has not learned assistant behavior. | |