VAGOsolutions/SauerkrautLM-14b-MoE-LaserChat

VAGO solutions SauerkrautLM-14b-MoE-LaserChat

Introducing SauerkrautLM-14b-MoE-LaserChat – our Sauerkraut (2x7b) 14b MoE version of the powerful SauerkrautLM-7b-LaserChat and yam-peleg/Experiment26-7B !

By combining the two models, we were able to significantly increase both the German and English language skills. In addition, the initial SauerkrautLM-7b-LaserChat also acts as an adapter for Experiment26-7B, which means it benefits from the chat capabilities of the SauerkrautLM-7b-LaserChat. At the same time, the SauerkrautLM-7b-LaserChat benefits from the knowledge and creativity of Experiment26-7B.

The model SauerkrautLM-14b-MoE-LaserChat is a joint effort between VAGO solutions and Hyperspace.ai. Much appreciation goes to the tremendous research effort of Fernando Fernandes Neto, David Golchinfar and Eric Hartford on their laserRMT approach. Without their independent research collaboration this model release would not have been possible.

Overview of all SauerkrautLM-14b-MoE-LaserChat models
Model Details
- Prompt template
Evaluation
Disclaimer
Contact
Collaborations
Acknowledgement

All SauerkrautLM-14b-MoE-LaserChat Models

Model	HF	GPTQ	GGUF	AWQ
SauerkrautLM-14b-MoE-LaserChat	Link	coming soon	coming soon	coming soon

Model Details

SauerkrautLM-14b-MoE-LaserChat

Model Type: SauerkrautLM-14b-MoE-LaserChat is a MoE Model based on SauerkrautLM-7b-LaserChat and yam-peleg/Experiment26-7B
Language(s): German, English
License: Apache 2.0
Contact: VAGO solutions, Hyperspace.computer

We improved the German language skills on this model further. Nevertheless, certain formulations may occur that are not entirely correct.

Prompt Template:

GPT4 Correct User: Hallo, wie geht es dir?<|end_of_turn|>GPT4 Correct Assistant: Hallo! Ich bin ein künstliches Intelligenzsystem und habe keine persönlichen Gefühle oder körperliche Zustände. Wie kann ich Ihnen helfen?<|end_of_turn|>GPT4 Correct User: Ich benötige nur einen kurzen Satz, den ich in das Prompt Template veröffentlichen kann.<|end_of_turn|>GPT4 Correct Assistant:

GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hello! How can I help you today? If you have any questions or need assistance, feel free to ask.<|end_of_turn|>GPT4 Correct User: I just need a short sentence to post in the prompt template.<|end_of_turn|>GPT4 Correct Assistant:

Evaluation

Open LLM Leaderboard:

benchmarked on lm-evaluation-harness 0.4.1

Metric	Value
Avg.	71.65
ARC (25-shot)	68.09
HellaSwag (10-shot)	84.78
MMLU (5-shot)	63.59
TruthfulQA (0-shot)	58.57
Winogrande (5-shot)	80.74
GSM8K (5-shot)	74.15

Performance

Model	AGIEval	GPT4All	TruthfulQA	BigBench	Average ⬇️
VAGOsolutions/SauerkrautLM-14b-MoE-LaserChat	44.38	74.76	58.57	47.98	56.42
VAGOsolutions/SauerkrautLM-Gemma-7b	37.5	72.46	61.24	45.33	54.13
zephyr-7b-beta	37.52	71.77	55.26	39.77	51.08
zephyr-7b-gemma-v0.1	34.22	66.37	52.19	37.10	47.47
google/gemma-7b-it	21.33	40.84	41.70	30.25	33.53

Details of AGIEval, GPT4All, TruthfulQA, BigBench

AGIEval

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
agieval_sat_math	1	none	None	acc	0.3727	±	0.0327
		none	None	acc_norm	0.3045	±	0.0311
agieval_sat_en_without_passage	1	none	None	acc	0.4806	±	0.0349
		none	None	acc_norm	0.4612	±	0.0348
agieval_sat_en	1	none	None	acc	0.7816	±	0.0289
		none	None	acc_norm	0.7621	±	0.0297
agieval_lsat_rc	1	none	None	acc	0.6134	±	0.0297
		none	None	acc_norm	0.6059	±	0.0298
agieval_lsat_lr	1	none	None	acc	0.5431	±	0.0221
		none	None	acc_norm	0.5216	±	0.0221
agieval_lsat_ar	1	none	None	acc	0.2435	±	0.0284
		none	None	acc_norm	0.2174	±	0.0273
agieval_logiqa_en	1	none	None	acc	0.3871	±	0.0191
		none	None	acc_norm	0.4101	±	0.0193
agieval_aqua_rat	1	none	None	acc	0.3031	±	0.0289
		none	None	acc_norm	0.2677	±	0.0278

Average: 44.38%

GPT4All

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
arc_challenge	1	none	None	acc	0.5947	±	0.0143
		none	None	acc_norm	0.6280	±	0.0141
arc_easy	1	none	None	acc	0.8506	±	0.0073
		none	None	acc_norm	0.8468	±	0.0074
boolq	2	none	None	acc	0.8761	±	0.0058
hellaswag	1	none	None	acc	0.6309	±	0.0048
		none	None	acc_norm	0.8323	±	0.0037
openbookqa	1	none	None	acc	0.326	±	0.0210
		none	None	acc_norm	0.470	±	0.0223
piqa	1	none	None	acc	0.8237	±	0.0089
		none	None	acc_norm	0.8335	±	0.0087
winogrande	1	none	None	acc	0.7466	±	0.0122

Average: 74.76%

TruthfulQA

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
truthfulqa_mc2	2	none	0	acc	0.5857	±	0.0141

Average: 58.57%

Bigbench

Tasks	Version	Filter	Metric	Value		Stderr
bbh_zeroshot_tracking_shuffled_objects_three_objects	2	flexible-extract	exact_match	0.3120	±	0.0294
bbh_zeroshot_tracking_shuffled_objects_seven_objects	2	flexible-extract	exact_match	0.1560	±	0.0230
bbh_zeroshot_tracking_shuffled_objects_five_objects	2	flexible-extract	exact_match	0.1720	±	0.0239
bbh_zeroshot_temporal_sequences	2	flexible-extract	exact_match	0.3960	±	0.0310
bbh_zeroshot_sports_understanding	2	flexible-extract	exact_match	0.8120	±	0.0248
bbh_zeroshot_snarks	2	flexible-extract	exact_match	0.5843	±	0.0370
bbh_zeroshot_salient_translation_error_detection	2	flexible-extract	exact_match	0.4640	±	0.0316
bbh_zeroshot_ruin_names	2	flexible-extract	exact_match	0.4360	±	0.0314
bbh_zeroshot_reasoning_about_colored_objects	2	flexible-extract	exact_match	0.5520	±	0.0315
bbh_zeroshot_navigate	2	flexible-extract	exact_match	0.5800	±	0.0313
bbh_zeroshot_movie_recommendation	2	flexible-extract	exact_match	0.7320	±	0.0281
bbh_zeroshot_logical_deduction_three_objects	2	flexible-extract	exact_match	0.5680	±	0.0314
bbh_zeroshot_logical_deduction_seven_objects	2	flexible-extract	exact_match	0.3920	±	0.0309
bbh_zeroshot_logical_deduction_five_objects	2	flexible-extract	exact_match	0.3960	±	0.0310
bbh_zeroshot_geometric_shapes	2	flexible-extract	exact_match	0.3800	±	0.0308
bbh_zeroshot_disambiguation_qa	2	flexible-extract	exact_match	0.6760	±	0.0297
bbh_zeroshot_date_understanding	2	flexible-extract	exact_match	0.4400	±	0.0315
bbh_zeroshot_causal_judgement	2	flexible-extract	exact_match	0.5882	±	0.0361

Average: 47.98%

Disclaimer

We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out. However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided. Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models.

Contact

If you are interested in customized LLMs for business applications, please get in contact with us via our websites. We are also grateful for your feedback and suggestions.

Collaborations

We are also keenly seeking support and investment for our startups, VAGO solutions and Hyperspace where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us at VAGO solutions, Hyperspace.computer

Acknowledgement

Many thanks to yam-peleg for providing such valuable model to the Open-Source community

VAGOsolutions/SauerkrautLM-14b-MoE-LaserChat