Gianloko/apex-coder-1.5b is a 1.5 billion parameter merged 16-bit model developed by Gianloko, fine-tuned for code generation. Trained using QLoRA and TRL SFTTrainer, it specializes in generating Apex code, particularly for Salesforce development. The model demonstrates a strong performance with an average LLM-as-judge score of 12.6/15 and a perplexity of 1.14, making it suitable for specific programming tasks.
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Gianloko/apex-coder-1.5bMost 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.