Dolphin3.0-Llama3.2-1B, developed by Eric Hartford, Ben Gitter, BlouseJury, and Cognitive Computations, is a 1 billion parameter instruct-tuned model based on the Llama 3.2 architecture. It is designed as a general-purpose local model, excelling in coding, mathematical tasks, agentic workflows, and function calling. This model prioritizes user control over system prompts and alignment, offering a steerable alternative to proprietary LLMs.
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dphn/Dolphin3.0-Llama3.2-1BMost 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.