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@@ -5,6 +5,31 @@ datasets:
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  language:
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  - en
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  pipeline_tag: text-generation
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # LocutusqueXFelladrin-TinyMistral248M-Instruct
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  This model was created by merging Locutusque/TinyMistral-248M-Instruct and Felladrin/TinyMistral-248M-SFT-v4 using mergekit. After the two models were merged, the resulting model was further trained on ~20,000 examples on the Locutusque/inst_mix_v2_top_100k at a low learning rate to further normalize weights. The following is the YAML config used to merge:
 
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  language:
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  - en
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  pipeline_tag: text-generation
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+ widget:
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+ - text: >-
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+ <|USER|> Design a Neo4j database and Cypher function snippet to Display
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+ Extreme Dental hygiene: Using Mouthwash for Analysis for Beginners.
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+ Implement if/else or switch/case statements to handle different conditions
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+ related to the Consent. Provide detailed comments explaining your control
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+ flow and the reasoning behind each decision. <|ASSISTANT|>
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+ - text: >-
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+ <|USER|> Write me a story about a magical place. <|ASSISTANT|>
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+ - text: >-
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+ <|USER|> Write me an essay about the life of George Washington <|ASSISTANT|>
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+ - text: >-
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+ <|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|>
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+ - text: >-
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+ <|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|>
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+ - text: >-
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+ <|USER|> How to manage a lazy employee: Address the employee verbally. Don't allow an employee's laziness or lack of enthusiasm to become a recurring issue. Tell the employee you're hoping to speak with them about workplace expectations and performance, and schedule a time to sit down together. Question: To manage a lazy employee, it is suggested to talk to the employee. True, False, or Neither? <|ASSISTANT|>
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+ inference:
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+ parameters:
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+ temperature: 0.4
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+ do_sample: True
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+ top_p: 0.34
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+ top_k: 30
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+ max_new_tokens: 250
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+ repetition_penalty: 1.15
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  ---
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  # LocutusqueXFelladrin-TinyMistral248M-Instruct
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  This model was created by merging Locutusque/TinyMistral-248M-Instruct and Felladrin/TinyMistral-248M-SFT-v4 using mergekit. After the two models were merged, the resulting model was further trained on ~20,000 examples on the Locutusque/inst_mix_v2_top_100k at a low learning rate to further normalize weights. The following is the YAML config used to merge: