google/gemma-3-27b-it-qat-q4_0-unquantized
The google/gemma-3-27b-it-qat-q4_0-unquantized model is a 27 billion parameter instruction-tuned multimodal language model from Google's Gemma 3 family, built from the same research as Gemini models. It handles text and image inputs, generating text outputs, and features a 128K context window with multilingual support across 140+ languages. This specific checkpoint is unquantized but designed for quantization-aware training (QAT) to maintain bfloat16 quality with reduced memory. It excels in diverse text generation, image understanding, and reasoning tasks, suitable 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.
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top_p
This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.
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top_k
This limits the number of top tokens to consider. Set to -1 to consider all tokens.
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frequency_penalty
This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.
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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.
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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.
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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.
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