Walter-SOLAR-11B/README.md

license, datasets, model-index

license	datasets	model-index
apache-2.0	KnutJaegersberg/Auton	name results Walter-SOLAR-11B task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 60.41 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Walter-SOLAR-11B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 84.86 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Walter-SOLAR-11B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 64.99 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Walter-SOLAR-11B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 44.88 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Walter-SOLAR-11B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 79.56 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Walter-SOLAR-11B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 0.99 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Walter-SOLAR-11B Open LLM Leaderboard

Walter is an unaligned, free thinking AI assistant that has been given time to think about things. It's trained on instruction datasets with open source licenses. It covers a lot of tasks, 2/3 of the samples are from large datasets like flan, but also other datasets. It knows a few tricks, shown by the prompting examples below:

General Prompt Examples:

### Instruction:
Please answer the following question: Process:  - The bat sends out sound waves from their mouth or nose - The sound waves hit an object - Echoes are produced - The echo returns to the bat's ears - The bat hears the echo - The bat can figure out where the object is located.  suppose No bats live here happens, how will it affect STRONGER echolocation.  Which of the following is the supposed perturbation?  - directly impacting a step of the process - indirectly impacting a step of the process - not impacting any step of the process Answer: 
### Response:
indirectly impacting a step of the process

### Instruction:
You will be given a text in Russian language which contain different emotion labels from the list - ['joy', ' sadness', 'surprise', 'fear', 'anger']. You need to output the incorrect emotion label, which is irrelevant to the input text. Your answer (i) should contain only one emotion label (ii) should be unambiguous. 
### Input:
То чувство, когда идешь домой после 3-х часовой тренировки и в подъезде пахнет запеченной курицей - зачем вы меня дразните :( 
### Response:
surprise

CoT Prompt Example:

### Instruction:
In this task, you are given a sentence with a missing word that can be an object, a person, and/or an action. Fill in the blank with a plausible word. Although each sentence has many correct answers, you only have to write one answer. PersonX puts ___ on PersonY's car 
### Reasoning:
Since PersonX is putting water on PersonY's car, it could be assumed that the car is on fire. 
### Response:
water

Summarization Example:

### Instruction:
Below is a text. Write a comprehensive, concise and coherent summary. 
### Input:
Embodied bilingual language, also known as L2 embodiment, is the idea that people mentally simulate their actions, perceptions, and emotions when speaking and understanding a second language (L2) as with their first language (L1).  It is closely related to embodied cognition and embodied language processing, both of which only refer to native language thinking and speaking.  An example of embodied bilingual language would be situation in which a L1 English speaker learning Spanish as a second language hears the word rápido ("fast") in Spanish while taking notes and then proceeds to take notes more quickly. ==Overview== Embodied bilingual language refers to the role ......
### Response:
Embodied bilingual language, also known as L2 embodiment, is the idea that people mentally simulate their actions, perceptions, and emotions when speaking and understanding a second language (L2) as with their first language (L1).  It is closely related to embodied cognition and embodied language processing, both of which only refer to native language thinking and speaking.  An example of embodied bilingual language would be situation in which a L1 English speaker learning Spanish as a second language hears the word rápido ("fast") in Spanish while taking notes and then proceeds to take notes more quickly.

Essay Example:

### Instruction:
Write the original essay for the following summary: A sleeveless dress with a round neck is a great day dress and can be worn for both formal and casual occasions. It has a sweetheart neckline and is made from 100% silk. The dress has a chiffon overlay that covers up the miniskirt and still adds to the class of the dress. 
### Response:
Sleeveless Dress with a Round Neck Essay Casual wear, day dress, more formal dress The garment is a sleeveless dress with a round neck and has a sweetheart neckline. It has sheer detail on the neckline and on the back. These aspects of design make it suitable to be worn for day and evening occasions. Its great detail and chicness make it suitable for more formal events, ....

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	55.95
AI2 Reasoning Challenge (25-Shot)	60.41
HellaSwag (10-Shot)	84.86
MMLU (5-Shot)	64.99
TruthfulQA (0-shot)	44.88
Winogrande (5-shot)	79.56
GSM8k (5-shot)	0.99