XformAI-india/Qwen3-1.7B-medicaldataset
XformAI-india/Qwen3-1.7B-medicaldataset is a 1.7 billion parameter causal language model, fine-tuned by XformAI-India from the Qwen3-1.7B base model. Optimized on a curated medical dataset, it excels at medical question answering, clinical documentation, and healthcare-related reasoning. This model is specifically designed for research and educational purposes in medical AI, leveraging a 40960 token context length.
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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