Instructions to use LeoLM/leo-hessianai-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeoLM/leo-hessianai-70b with Transformers:

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

pipe = pipeline("text-generation", model="LeoLM/leo-hessianai-70b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeoLM/leo-hessianai-70b")
model = AutoModelForCausalLM.from_pretrained("LeoLM/leo-hessianai-70b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LeoLM/leo-hessianai-70b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeoLM/leo-hessianai-70b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeoLM/leo-hessianai-70b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LeoLM/leo-hessianai-70b

SGLang

How to use LeoLM/leo-hessianai-70b 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 "LeoLM/leo-hessianai-70b" \
    --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": "LeoLM/leo-hessianai-70b",
		"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 "LeoLM/leo-hessianai-70b" \
        --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": "LeoLM/leo-hessianai-70b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LeoLM/leo-hessianai-70b with Docker Model Runner:
```
docker model run hf.co/LeoLM/leo-hessianai-70b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LAION LeoLM 70b: Linguistically Enhanced Open Language Model

Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release a series foundation models trained with 8k context length under the Llama-2 community license. Now, we're finally releasing the much anticipated leo-hessianai-70b, the largest model of this series based on Llama-2-70b. With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our blog post or our paper (preprint coming soon) for more details!

A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.

Model Details

Finetuned from: meta-llama/Llama-2-70b-hf
Model type: Causal decoder-only transformer language model
Language: English and German
License: LLAMA 2 COMMUNITY LICENSE AGREEMENT
Contact: LAION Discord or Björn Plüster

Use in 🤗Transformers

First install direct dependencies:

pip install transformers torch

Then load the model in transformers. Note that this requires lots of VRAM and most-likely multiple devices. Use load_in_8bit=True or load_in_4bit=True to save some memory by using a quantized version. For more quantized versions, check out our models at TheBloke's page: (coming soon!)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    model="LeoLM/leo-hessianai-70b",
    device_map="auto",
    torch_dtype=torch.bfloat16,
    use_flash_attention_2=False   # Set to true to use FA2. Requires `pip install flash-attn`
)