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cognitivecomputations/dolphin-2.6-mixtral-8x7b

The Dolphin 2.6 Mixtral 8x7b model is a finetuned version of the Mixtral-8x7b model, trained on a variety of data including coding data, for 3 days on 4 A100 GPUs. It is uncensored and requires trust_remote_code. The model is very obedient and good at coding, but not DPO tuned. The dataset has been filtered for alignment and bias. The model is compliant with user requests and can be used for various purposes such as generating code or engaging in general chat.

The Dolphin 2.6 Mixtral 8x7b model is a finetuned version of the Mixtral-8x7b model, trained on a variety of data including coding data, for 3 days on 4 A100 GPUs. It is uncensored and requires trust_remote_code. The model is very obedient and good at coding, but not DPO tuned. The dataset has been filtered for alignment and bias. The model is compliant with user requests and can be used for various purposes such as generating code or engaging in general chat.

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$0.27/Mtoken
ProjectLicense
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dolphin-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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