Llama 2 7b



The llama-2-7b model is a 7 billion parameter text-to-text transformer model that generates detailed textual narratives based on given prompts. The model takes multiple parameters such as 'top_k', 'top_p', 'temperature', 'max_new_tokens', 'min_new_tokens', 'repetition_penalty', 'repetition_penalty_sustain', and 'token_repetition_penalty_decay' to control the output production. The generated output is a sequence of tokens based on the input prompt, and it can range from single words to complete lines of a narrative. This model can hence be utilized for generating substantial and creative text.

Use cases

The Llama 2 7B is a base version of a 7 billion parameter language model that converts text-to-text. Its use cases are expansive, playing significant roles in sectors such as creative writing, education, entertainment, customer service, and data analytics. In creative writing, it could be used to generate storylines, articles or blog posts. The AI has immense potential in education where it can assist teachers in lesson planning, grading or delivering complex subject matters in a simplified manner. In entertainment, Llama 2 7B could be used in generating scripts for movies, TV shows or plays. Practically, it can be useful in customer service sectors where it offers automated customer responses, thereby reducing human input and improving efficiency. In data analytics, the model can be utilized to summarize large volumes of data text into concise information. Products that might arise from this AI model include automatic blog and article generators, AI teachers, virtual customer service representatives and scriptwriting tools.



Cost per run
Avg run time

Creator Models

Codellama 34b$?6,555
Llama 2 13b$?17,926
Llama 2 13b Chat$?1,592,213
Codellama 13b$?71,820

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Summary of this model and related resources.

Model NameLlama 2 7b
Base version of Llama 2 7B, a 7 billion parameter language model
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkNo paper link provided


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