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anemll-Llama-3.2-1B-Instruct-ctx1546_0.1.2

Core ML
ANE
Llama
Apple
Apple Neural Engine
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  1. README.md +130 -3
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README.md CHANGED
@@ -1,3 +1,130 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ tags:
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+ - coreml
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+ - ANE
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+ - Llama
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+ - Apple
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+ - Apple Neural Engine
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+ ---
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+
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+ # Model info
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+ This is a [Llama-3.2 1B Instruct model](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) converted to an [Anemll](https://github.com/Anemll/Anemll) model using Anemll 0.1.2 and with the context size set to 1546 (cannot be changed at runtime).
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+
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+ It was converted using the [convert_model.sh](https://github.com/Anemll/Anemll/blob/main/docs/convert_model.md) script
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+ and a *--context 1546* parameter.
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+
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+ #### Some things ⬇️
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+ - Anemll is in alpha, proceed at your own risk.
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+ - You will need to clone the [Anemll repo](https://github.com/Anemll/Anemll) to run this model (unlike the [Anemll HF models](https://huggingface.co/collections/anemll/anemll-011-67aa41b5ba1bcdd966a28fd0) that include runnable chat.py files).
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+ - Once you've downloaded both this model and the Anemll repo you can either follow the [chat instructions from the docs](https://github.com/Anemll/Anemll/blob/main/docs/chat.md) or run it using
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+ ```bash
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+ python full-path-to-anemll-repo/tests/chat.py \
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+ --meta full-path-to-this-model-repo/meta.yaml
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+ ```
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+ - Anemll models can only be ran with the Anemll library and on Apple silicon. DYOR if this model is for you or not. The Anemll library creators are active on X.
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+
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+
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+ ## *Below is the copy pasted README from [one of the original HF Anemll models](https://huggingface.co/collections/anemll/anemll-011-67aa41b5ba1bcdd966a28fd0). Follow the instructions untill the run part, where instead of running a chat.py file that does not exist in this repo, you will run a chat.py file from the cloned Anemll repo (see above).*
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+
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+ ***-----------------------------------------------------------------------------------------------------***
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+
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+ # ANEMLL
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+
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+ **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).
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+
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+ The goal is to provide a fully open-source pipeline from model conversion to inference for common LLM architectures running on ANE.
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+
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+ This enables seamless integration and on-device inference for low-power applications on edge devices, ensuring maximum privacy and security.
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+
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+ This is critical for autonomous applications, where models run directly on the device without requiring an internet connection.
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+
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+ ---
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+
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+ ## License
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+
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+ ANEMLL is licensed under the [MIT License](https://opensource.org/license/mit).
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+ The model is based on Meta's LLaMA 3.2 and may require a separate license.
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+
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+ This test model is exclusively for the Meta's LLaMA 3.2 1B (512 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.
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+
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+ ---
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+
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+ ## Requirements
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+
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+ - **macOS Sequoia** with Apple Neural Engine and 16GB RAM
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+ - **CoreML Tools** and **HuggingFace Transformers** libraries
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+ - **Python 3.9**
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+
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+ `chat.py` provides a sample inference script.
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+ `chat_full.py` provides a sample inference script with history and conversation management.
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+
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+ **Installation**
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+
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+ 1. Download the model from Hugging Face:
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+ ```bash
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+ # Install required tools
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+ pip install huggingface_hub
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+
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+ # Install Git LFS (Large File Support)
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+ # macOS with Homebrew:
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+ brew install git-lfs
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+ # Or Ubuntu/Debian:
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+ # sudo apt-get install git-lfs
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+
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+ # Initialize Git LFS
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+ git lfs install
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+
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+ # Clone the repository with model files
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+ git clone https://huggingface.co/anemll/anemll-Meta-Llama-3.2-1B-ctx512_0.1.1
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+ ```
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+
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+ 2. Extract model files:
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+ ```bash
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+ # Navigate to cloned directory
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+ cd anemll-Meta-Llama-3.2-1B-ctx512_0.1.1
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+
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+ # Pull LFS files (model weights)
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+ git lfs pull
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+
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+ # Extract CoreML model files
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+ find . -type f -name "*.zip" -exec unzip {} \;
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+ ```
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+
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+ 3. Install dependencies:
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+ ```bash
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+ pip install coremltools transformers
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+ ```
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+
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+ **Coremltools:**
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+
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+ See coremltools installation guide at https://coremltools.readme.io/v4.0/docs/installation
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+
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+ **How to Run (----READ TOP OF README AGAIN----)**
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+
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+ 1. Basic chat interface:
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+ ```bash
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+ python chat.py --meta ./meta.yaml
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+ ```
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+
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+ 2. Full conversation mode with history:
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+ ```bash
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+ python chat_full.py --meta ./meta.yaml
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+ ```
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+
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+ > Note: The first time the model loads, macOS will take some time to place it on the device.
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+ > Subsequent loads will be instantaneous.
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+ > Use Ctrl-D to exit, Ctrl-C to interrupt inference.
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+
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+
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+ **More Info**
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+ **More Info**
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+ Please check following links for later updates:
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+
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+ * [GitHub](https://github.com/anemll)
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+ * [Hugging Face Models](https://huggingface.co/anemll)
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+ * [Twitter/X](https://x.com/anemll)
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+ * [Website](https://anemll.com)
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+
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+
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+ realanemll@gmail.com
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+ tensor<int32, [3]> var_5 = const()[name = string("op_5"), val = tensor<int32, [3]>([0, 2, 1])];
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+ string var_29_pad_type_0 = const()[name = string("op_29_pad_type_0"), val = string("valid")];
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+ int32 var_29_groups_0 = const()[name = string("op_29_groups_0"), val = int32(1)];
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+ tensor<fp16, [16032, 2048, 1, 1]> var_9_promoted_to_fp16 = const()[name = string("op_9_promoted_to_fp16"), val = tensor<fp16, [16032, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
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+ tensor<int32, [1]> var_31_axes_0 = const()[name = string("op_31_axes_0"), val = tensor<int32, [1]>([2])];
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+ tensor<fp16, [1, 16032, 1]> var_31_cast_fp16 = squeeze(axes = var_31_axes_0, x = var_29_cast_fp16)[name = string("op_31_cast_fp16")];
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+ tensor<int32, [3]> var_34_perm_0 = const()[name = string("op_34_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
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+ string var_55_pad_type_0 = const()[name = string("op_55_pad_type_0"), val = string("valid")];
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+ tensor<int32, [2]> var_55_strides_0 = const()[name = string("op_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
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+ tensor<int32, [4]> var_55_pad_0 = const()[name = string("op_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
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+ int32 var_55_groups_0 = const()[name = string("op_55_groups_0"), val = int32(1)];
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+ tensor<fp16, [16032, 2048, 1, 1]> var_35_promoted_to_fp16 = const()[name = string("op_35_promoted_to_fp16"), val = tensor<fp16, [16032, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65667200)))];
25
+ tensor<fp16, [1, 16032, 1, 1]> var_55_cast_fp16 = conv(dilations = var_55_dilations_0, groups = var_55_groups_0, pad = var_55_pad_0, pad_type = var_55_pad_type_0, strides = var_55_strides_0, weight = var_35_promoted_to_fp16, x = input_cast_fp16)[name = string("op_55_cast_fp16")];
26
+ tensor<int32, [1]> var_57_axes_0 = const()[name = string("op_57_axes_0"), val = tensor<int32, [1]>([2])];
27
+ tensor<fp16, [1, 16032, 1]> var_57_cast_fp16 = squeeze(axes = var_57_axes_0, x = var_55_cast_fp16)[name = string("op_57_cast_fp16")];
28
+ tensor<int32, [3]> var_60_perm_0 = const()[name = string("op_60_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
29
+ string var_81_pad_type_0 = const()[name = string("op_81_pad_type_0"), val = string("valid")];
30
+ tensor<int32, [2]> var_81_strides_0 = const()[name = string("op_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
31
+ tensor<int32, [4]> var_81_pad_0 = const()[name = string("op_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
32
+ tensor<int32, [2]> var_81_dilations_0 = const()[name = string("op_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
33
+ int32 var_81_groups_0 = const()[name = string("op_81_groups_0"), val = int32(1)];
34
+ tensor<fp16, [16032, 2048, 1, 1]> var_61_promoted_to_fp16 = const()[name = string("op_61_promoted_to_fp16"), val = tensor<fp16, [16032, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131334336)))];
35
+ tensor<fp16, [1, 16032, 1, 1]> var_81_cast_fp16 = conv(dilations = var_81_dilations_0, groups = var_81_groups_0, pad = var_81_pad_0, pad_type = var_81_pad_type_0, strides = var_81_strides_0, weight = var_61_promoted_to_fp16, x = input_cast_fp16)[name = string("op_81_cast_fp16")];
36
+ tensor<int32, [1]> var_83_axes_0 = const()[name = string("op_83_axes_0"), val = tensor<int32, [1]>([2])];
37
+ tensor<fp16, [1, 16032, 1]> var_83_cast_fp16 = squeeze(axes = var_83_axes_0, x = var_81_cast_fp16)[name = string("op_83_cast_fp16")];
38
+ tensor<int32, [3]> var_86_perm_0 = const()[name = string("op_86_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
39
+ string var_107_pad_type_0 = const()[name = string("op_107_pad_type_0"), val = string("valid")];
40
+ tensor<int32, [2]> var_107_strides_0 = const()[name = string("op_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
41
+ tensor<int32, [4]> var_107_pad_0 = const()[name = string("op_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
42
+ tensor<int32, [2]> var_107_dilations_0 = const()[name = string("op_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
43
+ int32 var_107_groups_0 = const()[name = string("op_107_groups_0"), val = int32(1)];
44
+ tensor<fp16, [16032, 2048, 1, 1]> var_87_promoted_to_fp16 = const()[name = string("op_87_promoted_to_fp16"), val = tensor<fp16, [16032, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197001472)))];
45
+ tensor<fp16, [1, 16032, 1, 1]> var_107_cast_fp16 = conv(dilations = var_107_dilations_0, groups = var_107_groups_0, pad = var_107_pad_0, pad_type = var_107_pad_type_0, strides = var_107_strides_0, weight = var_87_promoted_to_fp16, x = input_cast_fp16)[name = string("op_107_cast_fp16")];
46
+ tensor<int32, [1]> var_109_axes_0 = const()[name = string("op_109_axes_0"), val = tensor<int32, [1]>([2])];
47
+ tensor<fp16, [1, 16032, 1]> var_109_cast_fp16 = squeeze(axes = var_109_axes_0, x = var_107_cast_fp16)[name = string("op_109_cast_fp16")];
48
+ tensor<int32, [3]> var_112_perm_0 = const()[name = string("op_112_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
49
+ string var_133_pad_type_0 = const()[name = string("op_133_pad_type_0"), val = string("valid")];
50
+ tensor<int32, [2]> var_133_strides_0 = const()[name = string("op_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
51
+ tensor<int32, [4]> var_133_pad_0 = const()[name = string("op_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
52
+ tensor<int32, [2]> var_133_dilations_0 = const()[name = string("op_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
53
+ int32 var_133_groups_0 = const()[name = string("op_133_groups_0"), val = int32(1)];
54
+ tensor<fp16, [16032, 2048, 1, 1]> var_113_promoted_to_fp16 = const()[name = string("op_113_promoted_to_fp16"), val = tensor<fp16, [16032, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262668608)))];
55
+ tensor<fp16, [1, 16032, 1, 1]> var_133_cast_fp16 = conv(dilations = var_133_dilations_0, groups = var_133_groups_0, pad = var_133_pad_0, pad_type = var_133_pad_type_0, strides = var_133_strides_0, weight = var_113_promoted_to_fp16, x = input_cast_fp16)[name = string("op_133_cast_fp16")];
56
+ tensor<int32, [1]> var_135_axes_0 = const()[name = string("op_135_axes_0"), val = tensor<int32, [1]>([2])];
57
+ tensor<fp16, [1, 16032, 1]> var_135_cast_fp16 = squeeze(axes = var_135_axes_0, x = var_133_cast_fp16)[name = string("op_135_cast_fp16")];
58
+ tensor<int32, [3]> var_138_perm_0 = const()[name = string("op_138_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
59
+ string var_159_pad_type_0 = const()[name = string("op_159_pad_type_0"), val = string("valid")];
60
+ tensor<int32, [2]> var_159_strides_0 = const()[name = string("op_159_strides_0"), val = tensor<int32, [2]>([1, 1])];
61
+ tensor<int32, [4]> var_159_pad_0 = const()[name = string("op_159_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
62
+ tensor<int32, [2]> var_159_dilations_0 = const()[name = string("op_159_dilations_0"), val = tensor<int32, [2]>([1, 1])];
63
+ int32 var_159_groups_0 = const()[name = string("op_159_groups_0"), val = int32(1)];
64
+ tensor<fp16, [16032, 2048, 1, 1]> var_139_promoted_to_fp16 = const()[name = string("op_139_promoted_to_fp16"), val = tensor<fp16, [16032, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328335744)))];
65
+ tensor<fp16, [1, 16032, 1, 1]> var_159_cast_fp16 = conv(dilations = var_159_dilations_0, groups = var_159_groups_0, pad = var_159_pad_0, pad_type = var_159_pad_type_0, strides = var_159_strides_0, weight = var_139_promoted_to_fp16, x = input_cast_fp16)[name = string("op_159_cast_fp16")];
66
+ tensor<int32, [1]> var_161_axes_0 = const()[name = string("op_161_axes_0"), val = tensor<int32, [1]>([2])];
67
+ tensor<fp16, [1, 16032, 1]> var_161_cast_fp16 = squeeze(axes = var_161_axes_0, x = var_159_cast_fp16)[name = string("op_161_cast_fp16")];
68
+ tensor<int32, [3]> var_164_perm_0 = const()[name = string("op_164_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
69
+ string var_185_pad_type_0 = const()[name = string("op_185_pad_type_0"), val = string("valid")];
70
+ tensor<int32, [2]> var_185_strides_0 = const()[name = string("op_185_strides_0"), val = tensor<int32, [2]>([1, 1])];
71
+ tensor<int32, [4]> var_185_pad_0 = const()[name = string("op_185_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
72
+ tensor<int32, [2]> var_185_dilations_0 = const()[name = string("op_185_dilations_0"), val = tensor<int32, [2]>([1, 1])];
73
+ int32 var_185_groups_0 = const()[name = string("op_185_groups_0"), val = int32(1)];
74
+ tensor<fp16, [16032, 2048, 1, 1]> var_165_promoted_to_fp16 = const()[name = string("op_165_promoted_to_fp16"), val = tensor<fp16, [16032, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394002880)))];
75
+ tensor<fp16, [1, 16032, 1, 1]> var_185_cast_fp16 = conv(dilations = var_185_dilations_0, groups = var_185_groups_0, pad = var_185_pad_0, pad_type = var_185_pad_type_0, strides = var_185_strides_0, weight = var_165_promoted_to_fp16, x = input_cast_fp16)[name = string("op_185_cast_fp16")];
76
+ tensor<int32, [1]> var_187_axes_0 = const()[name = string("op_187_axes_0"), val = tensor<int32, [1]>([2])];
77
+ tensor<fp16, [1, 16032, 1]> var_187_cast_fp16 = squeeze(axes = var_187_axes_0, x = var_185_cast_fp16)[name = string("op_187_cast_fp16")];
78
+ tensor<int32, [3]> var_190_perm_0 = const()[name = string("op_190_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
79
+ string var_211_pad_type_0 = const()[name = string("op_211_pad_type_0"), val = string("valid")];
80
+ tensor<int32, [2]> var_211_strides_0 = const()[name = string("op_211_strides_0"), val = tensor<int32, [2]>([1, 1])];
81
+ tensor<int32, [4]> var_211_pad_0 = const()[name = string("op_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
82
+ tensor<int32, [2]> var_211_dilations_0 = const()[name = string("op_211_dilations_0"), val = tensor<int32, [2]>([1, 1])];
83
+ int32 var_211_groups_0 = const()[name = string("op_211_groups_0"), val = int32(1)];
84
+ tensor<fp16, [16032, 2048, 1, 1]> var_191_promoted_to_fp16 = const()[name = string("op_191_promoted_to_fp16"), val = tensor<fp16, [16032, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459670016)))];
85
+ tensor<fp16, [1, 16032, 1, 1]> var_211_cast_fp16 = conv(dilations = var_211_dilations_0, groups = var_211_groups_0, pad = var_211_pad_0, pad_type = var_211_pad_type_0, strides = var_211_strides_0, weight = var_191_promoted_to_fp16, x = input_cast_fp16)[name = string("op_211_cast_fp16")];
86
+ tensor<int32, [1]> var_213_axes_0 = const()[name = string("op_213_axes_0"), val = tensor<int32, [1]>([2])];
87
+ tensor<fp16, [1, 16032, 1]> var_213_cast_fp16 = squeeze(axes = var_213_axes_0, x = var_211_cast_fp16)[name = string("op_213_cast_fp16")];
88
+ tensor<int32, [3]> var_216_perm_0 = const()[name = string("op_216_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
89
+ tensor<fp16, [1, 1, 16032]> logits8 = transpose(perm = var_216_perm_0, x = var_213_cast_fp16)[name = string("transpose_0")];
90
+ tensor<fp16, [1, 1, 16032]> logits7 = transpose(perm = var_190_perm_0, x = var_187_cast_fp16)[name = string("transpose_1")];
91
+ tensor<fp16, [1, 1, 16032]> logits6 = transpose(perm = var_164_perm_0, x = var_161_cast_fp16)[name = string("transpose_2")];
92
+ tensor<fp16, [1, 1, 16032]> logits5 = transpose(perm = var_138_perm_0, x = var_135_cast_fp16)[name = string("transpose_3")];
93
+ tensor<fp16, [1, 1, 16032]> logits4 = transpose(perm = var_112_perm_0, x = var_109_cast_fp16)[name = string("transpose_4")];
94
+ tensor<fp16, [1, 1, 16032]> logits3 = transpose(perm = var_86_perm_0, x = var_83_cast_fp16)[name = string("transpose_5")];
95
+ tensor<fp16, [1, 1, 16032]> logits2 = transpose(perm = var_60_perm_0, x = var_57_cast_fp16)[name = string("transpose_6")];
96
+ tensor<fp16, [1, 1, 16032]> logits1 = transpose(perm = var_34_perm_0, x = var_31_cast_fp16)[name = string("transpose_7")];
97
+ } -> (logits1, logits2, logits3, logits4, logits5, logits6, logits7, logits8);
98
+ }
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meta.yaml ADDED
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+ model_info:
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+ name: anemll-Llama-3.2-1B-Instruct-ctx1546
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+ version: 0.1.1
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+ description: |
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+ Demonstarates running Llama-3.2-1B-Instruct on Apple Neural Engine
6
+ Context length: 1546
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+ Batch size: 64
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+ Chunks: 2
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+ license: MIT
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+ author: Anemll
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+ framework: Core ML
12
+ language: Python
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+ parameters:
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+ context_length: 1546
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+ batch_size: 64
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+ lut_embeddings: none
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+ lut_ffn: 4
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+ lut_lmhead: 6
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+ num_chunks: 2
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+ model_prefix: llama
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
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+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 131072,
2061
+ "tokenizer_class": "PreTrainedTokenizerFast"
2062
+ }