ANITA-NEXT-24B-Magistral-2506-VISION-ITA by m-polignano is a 24 billion parameter Thinking Vision Language Model built on the Mistral architecture. It merges textual layers from ANITA-NEXT-24B-Magistral-2506-ITA with vision layers from mistralai/Mistral-Small-3.1-24B-Instruct-2503. This multilingual model supports both English and Italian, with a focus on further fine-tuning for specific Italian tasks, and has a context length of 128k, degrading after 40k.
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m-polignano/ANITA-NEXT-24B-Magistral-2506-VISION-ITAMost 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.
top_p
This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.
top_k
This limits the number of top tokens to consider. Set to -1 to consider all tokens.
frequency_penalty
This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.
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.
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.