Instructions to use LSX-UniWue/LLaMmlein_120M_prerelease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LSX-UniWue/LLaMmlein_120M_prerelease with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LSX-UniWue/LLaMmlein_120M_prerelease")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_120M_prerelease")
model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_120M_prerelease")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LSX-UniWue/LLaMmlein_120M_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_120M_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_120M_prerelease",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LSX-UniWue/LLaMmlein_120M_prerelease

SGLang

How to use LSX-UniWue/LLaMmlein_120M_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_120M_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_120M_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_120M_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_120M_prerelease",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LSX-UniWue/LLaMmlein_120M_prerelease with Docker Model Runner:
```
docker model run hf.co/LSX-UniWue/LLaMmlein_120M_prerelease
```

A newer version of this model is available: LSX-UniWue/LLaMmlein_120M

LLäMmlein 120M

This is a German Tinyllama 120M language model trained from scratch using the Tinyllama codebase on the German portion of RedPajama V2. Find more details on our page and our preprint

Next to the final model, we publish intermediate training checkpoints for our base models as separate branches of the model repository. These can be accessed via the drop-down menu labeled "main" in the top left corner of the "Files and versions" section.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_120M")

tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_120M")

Performance

We evaluated our model on the SuperGLEBer benchmark. Data Take Down

Downloads last month: 10

Safetensors

Model size

0.1B params

Tensor type

F32

Datasets used to train LSX-UniWue/LLaMmlein_120M_prerelease

Collection including LSX-UniWue/LLaMmlein_120M_prerelease

LLäMmlein 🐑

Collection

https://www.informatik.uni-wuerzburg.de/datascience/projects/nlp/llammlein/ • 10 items • Updated Mar 2 • 12

Paper for LSX-UniWue/LLaMmlein_120M_prerelease

LLäMmlein: Compact and Competitive German-Only Language Models from Scratch

Paper • 2411.11171 • Published Nov 17, 2024 • 8