ericflo/Llama-3.1-8B-ContinuedTraining2-FFT
ericflo/Llama-3.1-8B-ContinuedTraining2-FFT is an 8 billion parameter large language model developed by Eric Florenzano, based on the Meta-Llama-3.1-8B architecture with a 32768 token context length. This model utilizes full fine-tuning, rather than LoRA, for comprehensive learning across general text, code completion (especially Python), and instruction-following tasks. It excels in context-aware text infilling through its Fill-in-the-Middle (FIM) capabilities, including advanced Meta-FIM for complex, nested contexts. The model is primarily designed for text generation, code completion, and instruction following, with a focus on enhanced contextual understanding.
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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