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Improve model card with library name and GitHub link

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This PR improves the model card by adding the `library_name: transformers` metadata, clarifying the model's compatibility with the Hugging Face `transformers` library. It also includes a link to the GitHub repository containing the LongRoPE code. This enhances the model card's completeness and user-friendliness.

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  1. README.md +25 -8
README.md CHANGED
@@ -1,19 +1,28 @@
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  ---
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- license: mit
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- license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE
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-
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  language:
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  - en
 
 
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  pipeline_tag: text-generation
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  tags:
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  - nlp
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  - code
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  widget:
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- - messages:
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- - role: user
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- content: Can you provide ways to eat combinations of bananas and dragonfruits?
 
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  ---
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- 🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)
 
 
 
 
 
 
 
 
 
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  ## Model Summary
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@@ -149,7 +158,13 @@ You are a helpful travel assistant.<|end|>
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  <|user|>
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  I am going to Paris, what should I see?<|end|>
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  <|assistant|>
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- Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|>
 
 
 
 
 
 
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  <|user|>
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  What is so great about #1?<|end|>
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  <|assistant|>
@@ -343,3 +358,5 @@ The model is licensed under the [MIT license](https://huggingface.co/microsoft/P
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  ## Trademarks
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  This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
 
 
 
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  ---
 
 
 
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  language:
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  - en
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+ license: mit
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+ license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE
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  pipeline_tag: text-generation
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  tags:
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  - nlp
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  - code
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  widget:
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+ - messages:
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+ - role: user
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+ content: Can you provide ways to eat combinations of bananas and dragonfruits?
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+ library_name: transformers
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  ---
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+
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+ # LongRoPE2: Near-Lossless LLM Context Window Scaling
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+ The model was presented in the paper [LongRoPE2: Near-Lossless LLM Context Window Scaling](https://hf.co/papers/2502.20082).
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+
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+ # Paper abstract
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+ The abstract of the paper is the following:
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+ LongRoPE2 is a novel approach that extends the effective context window of pre-trained large language models (LLMs) to the target length, while preserving the performance on the original shorter context window. This is achieved by three contributions: (1) a hypothesis that insufficient training in higher RoPE dimensions contributes to the persistent out-of-distribution (OOD) issues observed in existing methods; (2) an effective RoPE rescaling algorithm that adopts evolutionary search guided by "needle-driven" perplexity to address the insufficient training problem; (3) a mixed context window training approach that fine-tunes model weights to adopt rescaled RoPE for long-context sequences while preserving the short-context performance with the original RoPE. Extensive experiments on LLaMA3-8B and Phi3-mini-3.8B across various benchmarks validate the hypothesis and demonstrate the effectiveness of LongRoPE2. Remarkably, LongRoPE2 extends LLaMA3-8B to achieve a 128K effective context length while retaining over 98.5% of short-context performance, using only 10B tokens -- 80x fewer than Meta's approach, which fails to reach the target effective context length. Code will be available at https://github.com/microsoft/LongRoPE.
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  ## Model Summary
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  <|user|>
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  I am going to Paris, what should I see?<|end|>
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  <|assistant|>
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+ Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:
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+
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+ 1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.
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+ 2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.
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+ 3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.
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+
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+ These are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|>
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  <|user|>
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  What is so great about #1?<|end|>
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  <|assistant|>
 
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  ## Trademarks
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  This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
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+ Code: https://github.com/microsoft/LongRoPE