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mistralai/Mixtral-8x7B-Instruct-v0.1

Mixtral mixture of expert model from Mistral AI. This is new experimental machine learning model using a mixture 8 of experts (MoE) 7b models. It was released as a torrent and the implementation is currently experimenta.

Mixtral mixture of expert model from Mistral AI. This is new experimental machine learning model using a mixture 8 of experts (MoE) 7b models. It was released as a torrent and the implementation is currently experimenta.

Public
$0.27/Mtoken
mistralai/Mixtral-8x7B-Instruct-v0.1 cover image

Mixtral 8x7b

 

0.00s

Tweak the overall style and tone of the conversation by giving some 'master' instructions. (Default: Be a helpful assistant)

maximum length of the newly generated generated text.If not set or None defaults to model's max context length minus input length. (Default: 512, 1 ≤ max_new_tokens ≤ 100000)

Temperature

temperature to use for sampling. 0 means the output is deterministic. Values greater than 1 encourage more diversity (Default: 0.7, 0 ≤ temperature ≤ 1)

Sample from the set of tokens with highest probability such that sum of probabilies is higher than p. Lower values focus on the most probable tokens.Higher values sample more low-probability tokens (Default: 0.9, 0 < top_p ≤ 1)

Sample from the best k (number of) tokens. 0 means off (Default: 0, 0 ≤ top_k < 100000)

Repetition Penalty

repetition penalty. Value of 1 means no penalty, values greater than 1 discourage repetition, smaller than 1 encourage repetition. (Default: 1, 0.01 ≤ repetition_penalty ≤ 5)

Up to 16 strings that will terminate generation immediately. Please separate items by comma

Presence Penalty

Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. (Default: 0, -2 ≤ presence_penalty ≤ 2)

Frequency Penalty

Positive values penalize new tokens based on how many times they appear in the text so far, increasing the model's likelihood to talk about new topics. (Default: 0, -2 ≤ frequency_penalty ≤ 2)

 


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