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- Model creator: meta-llama - Original model: meta-llama/Meta-Llama-3.1-405B-Instruct > llama.cpp/llama-cli -m Meta-Llama-3.1-405B-Instruct.Q2_K.gguf-00001-of-00009.gguf -p "write 10 sentences ending with the word apple." -n 1024 -t 40 GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

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- Model creator: meta-llama - Original model: meta-llama/Meta-Llama-3.1-70B-Instruct GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF:

Text generation
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- Model creator: meta-llama - Original model: meta-llama/Meta-Llama-3.1-8B-Instruct GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF:

Text generation
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Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

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- Model creator: meta-llama - Original model: meta-llama/Meta-Llama-3-8B-Instruct The GGUF and quantized models here are based on meta-llama/Meta-Llama-3-8B-Instruct model You can download only the quants you need instead of cloning the entire repository as follows:

Text generation
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