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Update README.md

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Omar Sanseviero 2024-03-01 23:44:38 +01:00 committed by GitHub
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@ -65,15 +65,25 @@ winget install --id Kitware.CMake
winget install --id Microsoft.VisualStudio.2022.BuildTools --force --override "--passive --wait --add Microsoft.VisualStudio.Workload.VCTools;installRecommended --add Microsoft.VisualStudio.Component.VC.Llvm.Clang --add Microsoft.VisualStudio.Component.VC.Llvm.ClangToolset"
```
### Step 1: Obtain model weights and tokenizer from Kaggle
### Step 1: Obtain model weights and tokenizer from Kaggle or Hugging Face Hub
Visit [the Gemma model page on
Kaggle](https://www.kaggle.com/models/google/gemma) and select `Model Variations
Kaggle](https://www.kaggle.com/models/google/gemma/frameworks/gemmaCpp) and select `Model Variations
|> Gemma C++`. On this tab, the `Variation` dropdown includes the options below.
Note bfloat16 weights are higher fidelity, while 8-bit switched floating point
weights enable faster inference. In general, we recommend starting with the
`-sfp` checkpoints.
Alternatively, visit the [gemma.cpp](https://huggingface.co/models?other=gemma.cpp)
models on the Hugging Face Hub. First go the the model repository of the model of interest
(see recommendations below). Then, click the `Files and versions` tab and download the
model and tokenizer files. For programmatic downloading, if you have `huggingface_hub`
installed, you can also run:
```
huggingface-cli download google/gemma-2b-cpp --local-dir build/
```
2B instruction-tuned (`it`) and pre-trained (`pt`) models:
| Model name | Description |
@ -96,7 +106,7 @@ weights enable faster inference. In general, we recommend starting with the
> **Important**: We strongly recommend starting off with the `2b-it-sfp` model to
> get up and running.
### Step 2: Extract Files
### Step 2: Extract Files (if downloading from Kaggle)
After filling out the consent form, the download should proceed to retrieve a
tar archive file `archive.tar.gz`. Extract files from `archive.tar.gz` (this can