Instructions to use LSX-UniWue/LLaMmlein_1B_prerelease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LSX-UniWue/LLaMmlein_1B_prerelease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LSX-UniWue/LLaMmlein_1B_prerelease")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_1B_prerelease") model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_1B_prerelease", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LSX-UniWue/LLaMmlein_1B_prerelease with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LSX-UniWue/LLaMmlein_1B_prerelease" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LSX-UniWue/LLaMmlein_1B_prerelease", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LSX-UniWue/LLaMmlein_1B_prerelease
- SGLang
How to use LSX-UniWue/LLaMmlein_1B_prerelease 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 "LSX-UniWue/LLaMmlein_1B_prerelease" \ --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": "LSX-UniWue/LLaMmlein_1B_prerelease", "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 "LSX-UniWue/LLaMmlein_1B_prerelease" \ --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": "LSX-UniWue/LLaMmlein_1B_prerelease", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LSX-UniWue/LLaMmlein_1B_prerelease with Docker Model Runner:
docker model run hf.co/LSX-UniWue/LLaMmlein_1B_prerelease
| datasets: | |
| - togethercomputer/RedPajama-Data-V2 | |
| - LSX-UniWue/LLaMmlein-Dataset | |
| language: | |
| - de | |
| pipeline_tag: text-generation | |
| library_name: transformers | |
| license: other | |
| new_version: LSX-UniWue/LLaMmlein_1B | |
| # LLäMmlein 1B | |
| This is a German Tinyllama 1B language model trained from scratch using the [Tinyllama](https://github.com/jzhang38/TinyLlama) codebase on the German portion of [RedPajama V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2). | |
| Find more details on our [page](https://www.informatik.uni-wuerzburg.de/datascience/projects/nlp/llammlein/) and our [preprint](https://arxiv.org/abs/2411.11171)! | |
| ### Usage | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_1B") | |
| tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_1B") | |
| ``` | |
| ### Evaluation | |
| We evaluated our results on the [SuperGLEBer](https://lsx-uniwue.github.io/SuperGLEBer-site/) benchmark. | |
| [Data Take Down](https://www.informatik.uni-wuerzburg.de/datascience/projects/nlp/llammlein/) |