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+MyLlama2
+========
+
+This is a ground-up reimplementation of the Llama 2 family of large language models.
+It adheres to the exact same architecture, based on a decoder-only transformer model
+equipped with Group-Query Attention (GQA), key-value caching, SwiGLU feedforward layers,
+and SentencePiece embeddings. It is compatible with the original Llama 2 weights.
+Unlike Meta's model, this implementation does not incorporate parallel layers or any
+distributed processing APIs. Consequently, it can only run on a single GPU and is also
+capable of running on a CPU without the need for special tools (e.g., `torchrun`, `mpi`, etc.).
+
+This model was created for demonstration purposes, with the intent of sharing it with the
+community. During its development, I identified a few minor issues in FAIR's
+implementation, which I plan to contribute back through pull requests. I believe this
+implementation is more accessible for those new to AI, and I've included references to the papers
+where these concepts were first introduced. However, this code has not been extensively reviewed.
+For production projects, I recommend starting with [Meta's implementation](https://github.com/facebookresearch/llama).
+For high-performance CPU-only inference, consider compiling
+[llama.cpp](https://github.com/ggerganov/llama.cpp) while targeting the native architecture.
+
+Example usage:
+--------------
+
+```bash
+python inference_example.py \
+	llama/llama-2-7b \
+	./tokenizer.model \
+	--top_p 0.8 \
+	--max_generation_length 100 \
+	--context "Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal."
+```