EpistemeAI's Fireball-Alpaca-Llama3.1.07-8B-Philos-Math-KTO-beta is an 8 billion parameter Llama 3.1 based model, fine-tuned using the KTO (Kahneman-Tversky Optimization) method. Developed by EpistemeAI2, this model is optimized for reasoning and mathematical tasks, building upon the Fireball-Alpaca-Llama3.1.07-8B-Philos-Math base. It was trained 2x faster using Unsloth and Huggingface's TRL library, making it suitable for applications requiring efficient mathematical and philosophical reasoning.
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EpistemeAI/Fireball-Alpaca-Llama3.1.07-8B-Philos-Math-KTO-betaMost 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.