expertai/SLIMER-PARALLEL-LLaMA3
expertai/SLIMER-PARALLEL-LLaMA3 is an 8 billion parameter LLaMA-3 based language model specifically instructed for zero-shot Named Entity Recognition (NER) on English text. It significantly improves over previous SLIMER models, achieving a +17% performance increase and enabling parallel extraction of up to 16 Named Entities per prompt. This model excels at identifying never-seen-before Named Entity tags by leveraging prompt-enriched definitions and guidelines, making it highly effective for out-of-distribution NER tasks.
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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