google/medgemma-4b-it
MedGemma-4b-it is a 4.3 billion parameter instruction-tuned variant of Google's Gemma 3 model, specifically trained for performance on medical text and image comprehension. It utilizes a SigLIP image encoder pre-trained on diverse de-identified medical data, including chest X-rays, dermatology, ophthalmology, and histopathology images. This multimodal model excels at medical applications involving text generation, visual question answering, and report generation, outperforming base Gemma 3 models on clinically relevant benchmarks. It supports a long context length of at least 128K tokens for comprehensive medical data processing.
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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.
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.
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