Mistral-Nemo-Instruct-2407 is a 12 billion parameter instruction-tuned large language model developed jointly by Mistral AI and NVIDIA. It is based on the Mistral-Nemo-Base-2407 architecture, featuring a 32768 token context window and Grouped-Query Attention (GQA). This model is optimized for instruction following and excels across various benchmarks, including MMLU, HellaSwag, and multilingual tasks, making it suitable for general-purpose conversational AI and code-related applications.
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mistralai/Mistral-Nemo-Instruct-2407Most 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.