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README.md
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## LLama 3.2 1B Simplified
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This repo contains a simplified variant of the Llama 3.2 1B Instruct model, aimed at instruction for the [Introduction to Modern AI](https://modernaicourse.org) course. The model is intended for instructional purposes only, specifically meant to test the implementation of a Transformer for Homework 4.
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1. The model replaces RoPE with an absolute positional embedding. RoPE typically works slightly better, but is somewhat cumbersome and unintuitive to implement for an introductory class.
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2. The model uses normal multihead attention instead of grouped query attention. Grouped query attention is a minor architecturual optimization that introduces marginal added complexity with little instructional value.
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To build this model, we made these two architecture changes then finetuned the model to recover
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---
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license: llama3.2
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language:
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- en
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pipeline_tag: text-generation
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base_model: meta-llama/Llama-3.2-1B-Instruct
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base_model_relation: finetune
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datasets:
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- HuggingFaceFW/fineweb-edu
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- HuggingFaceH4/ultrachat_200k
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tags:
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- llama
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- llama-3.2
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- instruct
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- educational
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- original-format
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- multi-head-attention
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- absolute-positional-embeddings
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---
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## LLama 3.2 1B Simplified
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This repo contains a simplified variant of the Llama 3.2 1B Instruct model, aimed at instruction for the [Introduction to Modern AI](https://modernaicourse.org) course. The model is intended for instructional purposes only, specifically meant to test the implementation of a Transformer for Homework 4.
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1. The model replaces RoPE with an absolute positional embedding. RoPE typically works slightly better, but is somewhat cumbersome and unintuitive to implement for an introductory class.
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2. The model uses normal multihead attention instead of grouped query attention. Grouped query attention is a minor architecturual optimization that introduces marginal added complexity with little instructional value.
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To build this model, we made these two architecture changes and then finetuned the model to recover Llama 3.2 Instruct behavior using a KL distillation loss and next-token loss on a mixture of FineWebEDU (`HuggingFaceFW/fineweb-edu`, `sample-350BT`) and UltraChat200K (`HuggingFaceH4/ultrachat_200k`).
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