The Dria-Agent-a-7B model, developed by Dria, is a 7 billion parameter large language model built upon the Qwen2.5-Coder series, specifically Qwen/Qwen2.5-Coder-7B-Instruct. It is specialized for agentic applications, employing Pythonic function calling to enable complex, multi-step interactions with tools. This model excels at generating free-form reasoning and actions within Python code blocks, supporting one-shot parallel function calls and on-the-fly complex solution generation.
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driaforall/Dria-Agent-a-7BMost 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.