google/gemma-3-1b-it-qat-int4-unquantized
Gemma 3 1B IT QAT INT4 Unquantized is a 1 billion parameter instruction-tuned multimodal language model developed by Google DeepMind, part of the Gemma 3 family. This version is optimized for Quantization Aware Training (QAT) to preserve bfloat16 quality while reducing memory requirements, though the provided checkpoint is unquantized. It supports a 32K token context window and is designed for text generation and image understanding tasks, including question answering, summarization, and reasoning, making it suitable for resource-constrained 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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