arunvpp05/Nexura-Gemma2B
Nexura-Gemma-2B is a 2 billion parameter decoder-only transformer LLM, fine-tuned by arunvpp05 from Google's Gemma-2B base model. It undergoes a two-stage training process involving Supervised Fine-Tuning (SFT) on high-quality instruction datasets and Direct Preference Optimization (DPO) for alignment. This model is optimized for general-purpose text generation and instruction following, excelling in tasks like chat assistance, educational Q&A, and content rewriting, while requiring a strict XML-style instruction format.
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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