Llama-3.1-Korean-8B-InstructSh2orc
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8B Params FP8 Inference Available

sh2orc/Llama-3.1-Korean-8B-Instruct is an 8 billion parameter instruction-tuned language model, fine-tuned from Meta-Llama-3.1-8B-Instruct. This model is specifically optimized for Korean language understanding and generation, leveraging a 32768 token context length. It excels in conversational AI, question answering, and general text generation tasks in Korean, making it suitable for applications requiring robust Korean language capabilities.

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Parameters:8BContext length:32kArchitecture:TransformerPrecision:FP8Quantized variants:AvailableLast updated:July 2024
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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.

–

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.

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frequency_penalty

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

0.5

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.

0.5

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.

1.1

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.

0.05