Instructions to use LSX-UniWue/LLaMmlein_1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LSX-UniWue/LLaMmlein_1B with Transformers:

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

pipe = pipeline("text-generation", model="LSX-UniWue/LLaMmlein_1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_1B")
model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_1B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LSX-UniWue/LLaMmlein_1B 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"
# 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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LSX-UniWue/LLaMmlein_1B

SGLang

How to use LSX-UniWue/LLaMmlein_1B 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" \
    --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",
		"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" \
        --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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LSX-UniWue/LLaMmlein_1B with Docker Model Runner:
```
docker model run hf.co/LSX-UniWue/LLaMmlein_1B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LLäMmlein 1B

LLäMmlein 1B is a German LLaMa model trained from scratch using our adapted Tinyllama codebase on the German portion of RedPajama V2. To enhance data quality, we additionally deduplicated the dataset on paragraph level and filtered it using a token-to-word ratio filter. The resulting dataset can be found here.

We provide three model sizes:

Find more details on our page our page and our preprint!

Usage

You can use LLäMmlein with the transformers library. (Optional: install flash-attn to achieve highest efficiency.)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "LSX-UniWue/LLaMmlein_1B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Intermediate Checkpoints

In addition to the final model checkpoint, we publish intermediate checkpoints throughout the full training process as unique branches in this repository. A specific checkpoint can be loaded like this:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "LSX-UniWue/LLaMmlein_1B"
revision = "iter-00420000-ckpt"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
model = AutoModelForCausalLM.from_pretrained(model_id, revision=revision)

Next to the model itself each branch contains all datapoints that were used to train the model up to that point. In the correspinding folder, named after the checkpoint, you can find several .log files (depending on the number of GPUs) of the following format:

{"time": 1739809392.679516, 
  "iter_num": 0, 
  "data_id": ["sha1:EDQMBYDCYBLDAZH3MGYM276BM2DEHPPJ", "sha1:SAJCI75DRHZZFGQORV66NB5FVWUAVLFH", "sha1:7RBZV2MCEM4TUGBBWGTFQAKTWUOGETZU", "sha1:234M32IMLZF7455AKOFWDP6HT6YXAYB4", "sha1:2BIZ7LLSHRK5GUGPZM2GM55APTDKBUG2", "sha1:OF7OI77ZT7ROXGMB6LL4RSRANX7REAYK", "sha1:LGPUOCOV3MKETI5F3IHVGZPD4M26NNJL", "sha1:SHIHUW7FJTP5YHFFV2JZ2CAHUVMKK7XG"], 
  "file_id": [0, 0, 0, 0, 0, 0, 0, 0], 
  "process_rank": 0}