semvec/pypi-packages huggingface dataset:
DeepMap-1.0 1.1
The DeepMap is a deep learning based python package for genotype to phenotype mapping.
Requires Python: >=3.6

The DeepMap is a deep learning based python package for genotype to phenotype mapping in rice breeding and can be utilize for other prediction-based studies. It is based on multiple genetic-interactions approach for data augmentation and objects for the simplicity/reproducibility of the research by employing four lines of genomic prediction code. Please contact, ajay.ducs@gmail.com for more information.
sisu-gmail 0.0.4b0
A package to simplify my gmail api life
Requires Python: >=3.6

# sisu_gmail - abstractions of the Gmail API

Running the tests:

1) If this is your first test, make an **unused gmail address**.

2) Make sure to delete any welcome emails, etc.

3) git clone https://github.com/loren-magnuson/sisu_gmail && cd sisu_gmail
 
4) Put your Gmail API enabled credentials JSON in credentials.json.

5) python -m unittest

6) The Gmail API auth flow should start, complete it.

7) The auth token for your test email will be saved as token.json.

8) Tests should now attempt to complete.

9) Before your next test run, delete any leftover emails.
sisu-gmail-loren-magnuson 0.0.4a0
A package to simplify my gmail api life
Requires Python: >=3.6

# sisu_gmail - abstractions of the Gmail API

Running the tests:

1) If this is your first test, make an **unused gmail address**.

2) Make sure to delete any welcome emails, etc.

3) git clone https://github.com/loren-magnuson/sisu_gmail && cd sisu_gmail
 
4) Put your Gmail API enabled credentials JSON in credentials.json.

5) python -m unittest

6) The Gmail API auth flow should start, complete it.

7) The auth token for your test email will be saved as token.json.

8) Tests should now attempt to complete.

9) Before your next test run, delete any leftover emails.
sisU-gmail-loren-magnuson 0.0.3rc0
A package to simplify my gmail api life
Requires Python: >=3.6

# sisu_gmail - abstractions of the Gmail API

Running the tests:

1) If this is your first test, make an **unused gmail address**.

2) Make sure to delete any welcome emails, etc.

3) git clone https://github.com/loren-magnuson/sisu_gmail && cd sisu_gmail
 
4) Put your Gmail API enabled credentials JSON in credentials.json.

5) python -m unittest

6) The Gmail API auth flow should start, complete it.

7) The auth token for your test email will be saved as token.json.

8) Tests should now attempt to complete.

9) Before your next test run, delete any leftover emails.
commit5 0.1.1
Locally generate commit messages using CodeTrans and FastT5

# commit5
Automatically generate commit messages locally using T5. Currently using "SEBIS/code_trans_t5_small_commit_generation_transfer_learning_finetune", as it seems like the best quality for performance ratio (best T5-base model). Based on work of https://github.com/agemagician/CodeTrans, which uses data from https://github.com/epochx/commitgen.

### Installation and Usage
Ensure you have docker installed. Then run:
```sh
pip install commit5
commit5 download # Pulls the docker image
commit5 start # Starts the docker container
```

Wait around 10 seconds for the image to spin up to load the model into memory. Then run
```sh
commit5 test
```

Then, to automatically commit:
```sh
commit5 commit
```

Alternatively, to only generate messages, run
```sh
commit5 generate <diff_string>
```
Where diff_string is the string of the diff.

### Optimizations

FastT5 reduced the file sizes from 900mb (torch file) to a total of only 200mb (3 ONNX models) and the memory footprint to only 90mb. Further, the execution speed dropped from 1.3s to 0.5s.

```diff
diff --git a/grunt/tasks/release.js b/grunt/tasks/release.js
index efbb2e7..d377ee4 100644
--- a/grunt/tasks/release.js
+++ b/grunt/tasks/release.js
@@ -7,7 +7,7 @@ var grunt = require('grunt');
 
 var BOWER_PATH = '../react-bower/';
 var BOWER_GLOB = [BOWER_PATH + '*'];
-var BOWER_FILES = ['React.js', 'React.min.js', 'JSXTransformer.js'];
+var BOWER_FILES = ['react.js', 'react.min.js', 'JSXTransformer.js'];
 var GH_PAGES_PATH = '../react-gh-pages/';
 var GH_PAGES_GLOB = [GH_PAGES_PATH + '*'];
```

### Vision
I think in general there's a huge space for locally running LM-based devtools. There's generally huge privacy concerns in corporations around productivity tools for dev which is the problem around adoptivity of tools like Copilot/Codeium/Tabnine in companies, and I think powerful LM's can be built smaller while laptops are becoming beefier. There's also an assortment of optimizations (distillation + ONNX + quantization + llama.cpp) for running LLM's on lower-power machines.

### Archive
ONNX Runtime improves performance from 1.3s to 670ms per iteration, but the resulting model is bigger (1.7gb of three files vs 800mb in original). Model can be found at https://huggingface.co/kevinlu1248/ct-base-commits-onnx/. Tests were conducted on the following example.

### TODO
- [x] Figure out how to load in 16-bit or 8-bit properly
- [x] Turn into a Git tool
- [x] Cleanup codebase
- [ ] Upload to pypa
- [ ] Migrate to CodeTrans-small
- [ ] Add a grammar checker 
- [ ] Use CodeT5+