lastmass/Qwen3_Medical_GRPO
The lastmass/Qwen3_Medical_GRPO is a 4 billion parameter Qwen3-based language model developed by lastmass, fine-tuned specifically for the medical domain. It leverages multi-stage Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) with accuracy-based reward functions to enhance its medical knowledge, logical reasoning, and reliability. This model excels at understanding complex medical problems, providing detailed logical analysis, and delivering structured solutions in healthcare contexts.
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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