gss1147/Qwen3-0.6B-Sushi-Math-Code-Expert
The gss1147/Qwen3-0.6B-Sushi-Math-Code-Expert is an 0.8 billion parameter language model created by gss1147, merged using the SLERP method from Qwen3-0.6B-Sushi-Coder, math-stack_Qwen3-0, and Qwen3-0.6B-Code-Expert. This model is specifically designed and optimized for complex reasoning in mathematical and coding tasks, featuring an integrated 'thinking mode' for enhanced problem-solving. It is primarily intended for backend AI pipelines requiring robust performance in specialized math and code-related query handling.
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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