Llama-SEA-LION-v3-8B-IT is an 8 billion parameter instruction-tuned decoder-only language model developed by AI Singapore, built on the Llama 3.1 architecture with a 128k context length. It is specifically designed and instruction-tuned for Southeast Asian languages, including Indonesian, Javanese, Sundanese, Tamil, Thai, and Vietnamese, alongside English. This model excels in multilingual instruction-following and general language capabilities across a diverse set of SEA languages, evaluated using benchmarks like SEA-HELM, SEA-IFEval, and SEA-MTBench.
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aisingapore/Llama-SEA-LION-v3-8B-ITMost 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.