Hi-Satoh/adv_sft_dpo_final_14_merged is a 4 billion parameter Qwen3-based causal language model developed by Hi-Satoh. This model has been fine-tuned using Direct Preference Optimization (DPO) to enhance reasoning capabilities and structured response quality. It is specifically optimized for generating aligned outputs based on preferred data, making it suitable for tasks requiring improved logical coherence and format adherence.
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Hi-Satoh/adv_sft_dpo_final_14_mergedMost 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.