AesCoder-4B is a 4 billion parameter model developed by SamuelBang (Microsoft Research Asia, Shanghai Jiao Tong University, Peking University) specifically designed to enhance the aesthetic quality of LLM-generated code, particularly for webpage design. It was trained using the AesCode-358K dataset and an agentic reward feedback system, integrating executability, static, and interactive aesthetics. This model excels at generating visually appealing and functional web code, outperforming larger models like GPT-4o and GPT-4.1 on code aesthetics benchmarks.
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SamuelBang/AesCoder-4BMost 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.