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README

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-ANEMLL
-
-ANEMLL (pronounced like “animal”) is an open-source project
-focused on accelerating the porting of Large Language Models (LLMs)
-to tensor processors, starting with the Apple Neural Engine (ANE).
-
-The goal is to provide a fully open-source pipeline
-from model conversion to inference for common LLM architectures
-running on ANE.
-
-This enables seamless integration and on-device inference
-for low-power applications on edge devices,
-ensuring maximum privacy and security.
-
-This is critical for autonomous applications,
-where models run directly on the device
-without requiring an internet connection.
-
-License
-
-ANEMLL is licensed under the MIT License.
-https://opensource.org/license/mit
-The model is based on Meta’s LLaMA 3.2 and may require a separate license.
-
-
-
-This test model is exclusively for the Meta's LLaMA 3.2 1B (1024 context) model converted for CoreML,
-released before the official launch of the ANEMLL repository and minimal documentation.
-It is intended for early adopters only who requested an early release.
-
-
-Requirements
-    •	macOS Sequoia with Apple Neural Engine and 16GB RAM
-    •	CoreML Tools and HuggingFace Transformers libraries 
-    •	Python 3.9
-
-chat.py provides a sample inference script.
-We apologize for the current quality of chat.py and appreciate your patience.
-
-
-Prerequisites:
-pip install coremltools transformers
-
-How to RUN:
-python chat.py 
-
-Ctr-D to exit, Ctr-C to interrupt inference.
-
-alternative way to run:
-python chat.py S123 -d /path/to/anemll-LLAMA32-1B-ctx1024 ctx=1024
-
-The first time the model loads, macOS will take some time to place it on the device.
-Subsequent loads will be instantaneous.
-
-Please check following links for later updates:
-https://huggingface.co/anemll
-https://x.com/anemll
-https://github.com/anemll
-https://anemll.com
-
-[email protected]
-