google/gemma-3-27b-pt
Gemma 3 is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. The google/gemma-3-27b-pt model is a 27 billion parameter pre-trained variant, offering multimodal capabilities for text and image input with text output. It features a large 128K context window and multilingual support in over 140 languages. This model is well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning, and is designed for deployment in resource-limited environments.
Hugging Face Popular Sampler Settings
Most 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.
–