aws-prototyping/MegaBeam-Mistral-7B-512k
The aws-prototyping/MegaBeam-Mistral-7B-512k is a 7 billion parameter language model, based on Mistral-7B Instruct-v0.2, specifically engineered for efficient long-context processing. It supports an exceptionally large context window of 524,288 tokens, making it highly effective for tasks requiring extensive document analysis. This model excels at long-context retrieval and question answering, demonstrating strong performance on benchmarks like Needle In A Haystack and RULER.
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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