Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo
Saxo/Linkbricks-Horizon-AI-Llama-3.3-Korean-70B-sft-dpo is a 70 billion parameter language model developed by Yunsung Ji (Saxo) at Linkbricks, based on the meta-llama/Llama-3.3-70B-Instruct architecture. This model is specifically fine-tuned using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) with a 40 million Korean news and wiki corpus, alongside cross-lingual data for Korean, Japanese, Chinese, and English. It excels in high-dimensional analysis of customer reviews and social posts, coding, writing, mathematics, and complex logical reasoning, supporting a 32K context window and Function/Tool Calling.
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.
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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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