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Llama 2 Chat (13B) is a powerful language model capable of analyzing long and complex conversations with ease. Featuring a 4,100-token context window and 13 billion parameters, it's perfect for tasks that need deep reasoning and knowledge extraction. Designed for high-performance environments, this LLM ensures fast response times even with large data volumes.
License | LLAMA 2 Community License |
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Context window(in thousands) | 4096 |
Arena Elo | 1063 |
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MMLU | 53.6 |
MT Bench | 6.65 |
This model delivers average conversational quality, moderate reasoning abilities, and relatively low translation competency.
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The cost for running the model with Telnyx Inference is $0.0003 per 1,000 tokens. For instance, analyzing 1,000,000 customer chats, assuming each chat is 1,000 tokens long, would cost $300.
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The Llama-2-13b-chat-hf model is a large language model developed by Meta for chat and dialogue applications. It features 13 billion parameters, an optimized transformer architecture, and is trained on a diverse online dataset. It's designed for conversational AI, language services, and customer engagement, providing fast and detailed responses.
Llama-2-13b-chat-hf is smaller in size with 13 billion parameters compared to GPT-4, resulting in differences in conversational quality, reasoning abilities, and translation competency. While it may not excel in tasks requiring strong reasoning like GPT-4, it's tailored for high-performance dialogue use cases and offers a cost-effective solution for developers.
Yes, Llama-2-13b-chat-hf can be run on local GPU setups, including M1/M2 Macs, to achieve faster inference times. This makes it a versatile option for developers looking for efficient local deployment.
The primary use cases for Llama-2-13b-chat-hf include conversational AI, language services, and customer engagement tasks. It excels in generating new content, summarizing documents, and making simple evaluations, making it ideal for applications that require detailed and informative responses.
Llama-2-13b-chat-hf uses an optimized transformer architecture and is trained on publicly available online data. It employs supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety, ensuring it meets high standards for conversational AI applications.
You can start building with Llama-2-13b-chat-hf on Telnyx. Telnyx offers an inference service that enables developers to integrate this model into their connectivity apps efficiently. For more information and to get started, visit the Telnyx website.