SFR-Iterative-DPO-LLaMA-3-8B-R is an 8 billion parameter instruct model developed by Salesforce, based on the LLaMA-3 architecture with an 8192 token context length. It utilizes an iterative DPO-based online RLHF training method, enabling it to outperform models of similar size and many larger open-source and proprietary models on instruct benchmarks like Alpaca-Eval-V2, MT-Bench, and Chat-Arena-Hard. This model is optimized for instruction following and general conversational AI tasks, achieving strong performance without relying on additional human or GPT-4 labeling.
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TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-RMost 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.