KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024
KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024 is an 8 billion parameter instruction-tuned large language model developed by the Korea Institute of Science and Technology Information (KISTI). Built upon a merged base of Meta-Llama-3-8B and KISTI-KONI/KONI-Llama3.1-8B-20240824, it is specifically designed and optimized for tasks within science and technology domains. This model leverages Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) on specialized datasets, making it highly effective for scientific reasoning, mathematical problems, and technical writing with a 32768 token context length.
Hugging Face 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.
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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.
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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.
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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.
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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.
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