The yimingzhang/qwen-3-1.7b-57b-cool-from-66550-step96800 model is a 2 billion parameter language model based on the Qwen3-1.7B architecture, featuring 28 layers and a hidden size of 2048. With a vocabulary size of 2350 and a sequence length of 1024, this model is a fine-tuned variant of the Qwen3 series. Its specific differentiators and primary use cases are not detailed in the provided information, suggesting it may be an experimental or intermediate checkpoint.
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yimingzhang/qwen-3-1.7b-57b-cool-from-66550-step96800Most 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.