ECE-ILAB-Q1 is a 72.7 billion parameter instruction-tuned causal language model, developed by Louis Garcia, Matthieu Jollard, Andre-Louis Rochet, and Paul Lemaistre from ECE engineering school and TW3 Partners. This model is a merge of Qwen/Qwen2-72B-Instruct and cognitivecomputations/dolphin-2.9.2-qwen2-72b, designed to leverage the strengths of both base models. With a context length of 131072 tokens, it aims to provide robust performance across various tasks, as indicated by its evaluation on the Open LLM Leaderboard.
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paulml/ECE-ILAB-Q1Most 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.