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README.md
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This model was converted to GGUF format from [`DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B`](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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This model was converted to GGUF format from [`DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B`](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) for more details on the model.
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---
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Context : 1,000,000 tokens.
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Required: Llama 3 Instruct template.
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The Deep Hermes 8B Preview model (reasoning), [ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview ]
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converted to 1 million context using Nvidia's Ultra Long 1 million 8B Instruct model.
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The goal of this model was to stablize long generation and long context "needle in a haystack" issues.
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According to Nvidia there is both a bump in general performance, as well as perfect "recall" over the entire 1 million context.
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[ https://huggingface.co/nvidia/Llama-3.1-8B-UltraLong-1M-Instruct ]
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Additional changes:
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Model appears to be de-censored / more de-censored.
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Output generation is improved.
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Creative output generation is vastly improved.
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NOTE: Higher temps will result in deeper, richer "thoughts"... and frankly more interesting ones too.
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The "thinking/reasoning" tech (for the model at this repo) is from the original Llama 3.1 "DeepHermes" model from NousResearch:
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[ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview ]
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---
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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