oumi-ai/HallOumi-8B
HallOumi-8B by oumi-ai is an 8 billion parameter hallucination detection model, fine-tuned from Llama-3.1-8B-Instruct, with a 32768 token context length. It specializes in per-sentence verification of content, providing support determinations, confidence scores, relevant citations, and human-readable explanations. This model is optimized for building trust in AI systems by verifying outputs against known contexts, outperforming larger models like DeepSeek R1 and Claude Sonnet 3.5 in F1 score for hallucination detection.
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.
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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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