Hugging Face's logo

LeoLM
/
leo-hessianai-70b-chat

Text Generation
Transformers
PyTorch
English
German
llama
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
6

LAION LeoLM 70b Chat: 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.

LeoLM Chat

LeoLM/leo-hessianai-70b-chat is a German chat model built on our foundation model LeoLM/leo-hessianai-70b and finetuned on a selection of German instruction datasets. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores:

{
    "first_turn": 7.2375,
    "second_turn": 6.5375,
    "categories": {
        "writing": 8.55,
        "roleplay": 7.15,
        "reasoning": 4.2,
        "math": 4.85,
        "coding": 4.85,
        "extraction": 7.75,
        "stem": 8.45,
        "humanities": 9.3
    },
    "average": 6.8875
}

Have a look at some examples in this Google Doc.

Model Details

Use in 🤗Transformers

First install direct dependencies:

pip install transformers torch

Then load the model in transformers:

from transformers import pipeline
import torch

system_prompt = """<|im_start|>system
Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|>

"""
prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist."

generator = pipeline(model="LeoLM/leo-hessianai-70b-chat", device="cuda", torch_dtype=torch.float16)
print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192))

"Hamburg verfügt über ein umfangreiches Netzwerk an Radwegen, das ein effizientes und angenehmes Radfahren in der ganzen Stadt ermöglicht. Die Radwege variieren in Bezug auf ihre Qualität, wobei einige erstklassig mit eigens für Radfahrer reservierten Spuren sind, während andere einfache Fahrradsymbole auf dem Boden haben, die anzeigen, dass Radfahrer abwechselnd mit dem Autoverkehr auf der Straße fahren sollten. Einige Nebenstraßen haben auch spezielle Fahrradspuren, wobei einige mit Bordsteinabsenkungen zur Seite der Autospuren markiert sind. Zusätzlich haben viele Hauptstraßen, insbesondere in Nebenstraßen, fahrradfreundliche Abstände zwischen den geparkten Autos und dem Gehweg, was ein bequemes Fahren auf der Straße ermöglicht. Der Bau von Radschnellwegen, die schnelles und effizientes Radfahren in und aus der Stadt ermöglichen, ist im Gange und wird in den kommenden Jahren fortgesetzt. Insgesamt sind die Radwege in Hamburg weitläufig und gut ausgeschildert, was es zu einem angenehmen Ort macht, um mit dem Fahrrad zu fahren."

Prompting / Prompt Template

Prompt dialogue template (ChatML format):

"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
"""

The model input can contain multiple conversation turns between user and assistant, e.g.

<|im_start|>user
{prompt 1}<|im_end|>
<|im_start|>assistant
{reply 1}<|im_end|>
<|im_start|>user
{prompt 2}<|im_end|>
<|im_start|>assistant
(...)

Ethical Considerations and Limitations

LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of LeoLM/leo-hessianai-70b-chat cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of LeoLM/leo-hessianai-70b-chat, developers should perform safety testing and tuning tailored to their specific applications of the model.

We are aware of the model refusing to answer more often than desired. This will be adressed in future versions. For now, the training dataset is equal to that used for our smaller chat variants.

Please see Meta's Responsible Use Guide.

Finetuning Details

Hyperparameter Value
Num epochs 3
Examples per epoch 131214
Global batch size 256
Learning rate 1.5e-5
Warmup steps 15
LR scheduler Cosine
Adam betas (0.9, 0.95)
Weight Decay 0.01

Dataset Details 

## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%))
-----------------
  Accepted: 3534/3534 (100.0%)
  Accepted tokens: 2259302
  Skipped: 0 (0.0%)
  Min tokens per sample: 29
  Max tokens per sample: 2484
  Avg tokens per sample: 639.3044708545557
-----------------

## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%))
-----------------
  Accepted: 57841/57841 (100.0%)
  Accepted tokens: 42958192
  Skipped: 0 (0.0%)
  Min tokens per sample: 33
  Max tokens per sample: 5507
  Avg tokens per sample: 742.6944900675991
-----------------

## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%))
-----------------
  Accepted: 48969/48969 (100.0%)
  Accepted tokens: 13372005
  Skipped: 0 (0.0%)
  Min tokens per sample: 19
  Max tokens per sample: 1359
  Avg tokens per sample: 273.07082031489307
-----------------

## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%))
-----------------
  Accepted: 21314/21314 (100.0%)
  Accepted tokens: 8134690
  Skipped: 0 (0.0%)
  Min tokens per sample: 25
  Max tokens per sample: 1202
  Avg tokens per sample: 381.65947264708643
-----------------

## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%))
-----------------
  Accepted: 490/490 (100.0%)
  Accepted tokens: 618642
  Skipped: 0 (0.0%)
  Min tokens per sample: 747
  Max tokens per sample: 1678
  Avg tokens per sample: 1262.534693877551
-----------------

## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%))
-----------------
  Accepted: 392/392 (100.0%)
  Accepted tokens: 187897
  Skipped: 0 (0.0%)
  Min tokens per sample: 231
  Max tokens per sample: 826
  Avg tokens per sample: 479.3290816326531
-----------------

## Stats for 'total' (132540 samples (100.0%))
-----------------
  Accepted: 132540/132540 (100.0%)
  Accepted tokens: 67530728
  Skipped: 0 (0.0%)
  Min tokens per sample: 19
  Max tokens per sample: 5507
  Avg tokens per sample: 509.51205673758864
-----------------
Downloads last month
437
Inference Examples
Text Generation
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for LeoLM/leo-hessianai-70b-chat

Quantizations
4 models

Datasets used to train LeoLM/leo-hessianai-70b-chat