georgesung/llama2_7b_chat_uncensored
The georgesung/llama2_7b_chat_uncensored model is a 7 billion parameter Llama-2 architecture fine-tuned by georgesung. It was trained on an uncensored Wizard-Vicuna conversation dataset using QLoRA, optimized for open-ended, unfiltered conversational responses. This model is designed for applications requiring less restrictive or more direct AI interactions, offering a distinct conversational style compared to standard Llama-2 chat models. It supports a 4096-token context length and is available in fp16 HuggingFace 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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