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README.md CHANGED
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  ---
 
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  language:
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  - en
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  - fr
@@ -10,7 +11,6 @@ language:
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  - zh
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  - ja
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  license: apache-2.0
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- base_model: mistralai/Mistral-Nemo-Instruct-2407
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  model_name: Mistral-Nemo-Instruct-2407-GGUF
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  extra_gated_description: If you want to learn more about how we process your personal
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  data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
@@ -31,7 +31,7 @@ parameter_count: 12247782400
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  - **Original:** [Mistral Nemo Instruct 2407 13B](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)
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  - **Date Created:** 2024-07-17
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  - **Trained Context:** 1024000 tokens
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- - **Description:** Mistral's state-of-the-art 12B model with 128k context length, built in collaboration with NVIDIA.
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  ***
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  ## What is a GGUF?
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  GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware.
 
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  ---
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  language:
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  - en
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  - fr
 
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  - zh
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  - ja
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  license: apache-2.0
 
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  model_name: Mistral-Nemo-Instruct-2407-GGUF
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  extra_gated_description: If you want to learn more about how we process your personal
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  data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
 
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  - **Original:** [Mistral Nemo Instruct 2407 13B](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)
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  - **Date Created:** 2024-07-17
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  - **Trained Context:** 1024000 tokens
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+ - **Description:** The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-Nemo-Base-2407. Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.
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  ***
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  ## What is a GGUF?
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  GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware.