Qwen2-0.5B-Instruct is a 0.5 billion parameter instruction-tuned causal language model from the Qwen2 series, developed by Qwen. Built on a Transformer architecture with SwiGLU activation and group query attention, it features an improved tokenizer for multilingual and code adaptability. This model demonstrates competitive performance across benchmarks for language understanding, generation, coding, mathematics, and reasoning, making it suitable for a wide range of general-purpose conversational AI applications.
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123-cao/Qwen2-0.5B-InstructMost 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.