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Dolphin-Mistral-24B-Venice-EditionDphn
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24B Params FP8 Open Weights Inference Available

Dolphin Mistral 24B Venice Edition is a 24 billion parameter Mistral-based language model developed collaboratively by dphn and Venice.ai, featuring a 32768 token context length. This model is specifically designed to be uncensored and highly steerable, allowing users full control over system prompts and alignment. It aims to provide a general-purpose AI tool that prioritizes user control and data privacy, making it suitable for applications requiring custom ethical guidelines and consistent model behavior.

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Parameters:24BContext length:32kArchitecture:TransformerPrecision:FP8Quantized variants:AvailableLast updated:June 2025
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dphn/Dolphin-Mistral-24B-Venice-Edition
Popular Sampler Settings

Most commonly used values from Featherless users

temperature

This setting influences the sampling randomness. Lower values make the model more deterministic; higher values introduce randomness. Zero is greedy sampling.

0.8

top_p

This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.

1

top_k

This limits the number of top tokens to consider. Set to -1 to consider all tokens.

40

frequency_penalty

This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.

1.16

presence_penalty

This setting penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens; < 0 encourages repetition.

repetition_penalty

This setting penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens; < 1 encourages repetition.

1

min_p

This setting representing the minimum probability for a token to be considered relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.