UCSB-SURFI/VulnLLM-R-7B
UCSB-SURFI/VulnLLM-R-7B is the first specialized 7 billion parameter reasoning Large Language Model developed by UCSB-SURFI for software vulnerability detection. Unlike traditional tools, it generates a "Chain-of-Thought" to analyze why a vulnerability exists, mimicking human security auditing. This model excels at identifying complex logic vulnerabilities across C, C++, Python, and Java, outperforming larger general-purpose models and industry-standard tools.
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.
–
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.
–