Add A cli version

README.md

@@ -1,9 +1,15 @@
-# 🦙 Simple LLaMA Finetuner
+# 🦙 Simple LLaMA Finetuner - CLI
+
+
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lxe/simple-llama-finetuner/blob/master/Simple_LLaMA_FineTuner.ipynb)
 [![](https://img.shields.io/badge/no-bugs-brightgreen.svg)](https://github.com/lxe/no-bugs) 
 [![](https://img.shields.io/badge/coverage-%F0%9F%92%AF-green.svg)](https://github.com/lxe/onehundred/tree/master)
 
+
+
+## Original README.md
+
 Simple LLaMA Finetuner is a beginner-friendly interface designed to facilitate fine-tuning the [LLaMA-7B](https://github.com/facebookresearch/llama) language model using [LoRA](https://arxiv.org/abs/2106.09685) method via the [PEFT library](https://github.com/huggingface/peft) on commodity NVIDIA GPUs. With small dataset and sample lengths of 256, you can even run this on a regular Colab Tesla T4 instance.
 
 With this intuitive UI, you can easily manage your dataset, customize parameters, train, and evaluate the model's inference capabilities.
@@ -71,11 +77,73 @@ After training is done, navigate to "Inference" tab, click "Reload Models", sele
 
 Have fun!
 
+
 ## Screenshots
 
 |![Image1](https://user-images.githubusercontent.com/1486609/226793136-84531388-4081-49bb-b982-3f47e6ec25cd.png) | ![Image2](https://user-images.githubusercontent.com/1486609/226809466-b1eb6f3f-4049-4a41-a2e3-52b06a6e1230.png) |
 |:---:|:---:|
 
+## Cli version
+
+- This is a cli version of [simple-llama-finetuner](https://github.com/lxe/simple-llama-finetuner/).
+- This cli version is provided by [@chaignc](https://twitter.com/chaignc) from [Hacker AI Team](https://hacker-ai.ai).
+- Thank you to the original author [@lxe](https://twitter.com/lxe), who did the real work :D
+
+### Usage:
+
+```
+$ ./main_cli.py -h
+Usage: main_cli.py command [args...]
+
+Commands:
+ predict
+ train
+
+$ ./main_cli.py train -h
+Usage: main-cli.py train [OPTIONS]
+
+Options:
+ --epochs=INT (default: 1)
+ --gradient-accumulation-steps=INT (default: 1)
+ --learning-rate=FLOAT (default: 0.0003)
+ --lora-alpha=INT (default: 16)
+ --lora-dropout=FLOAT (default: 0.01)
+ --lora-r=INT (default: 8)
+ --max-seq-length=INT (default: 512)
+ --micro-batch-size=INT (default: 1)
+ --model-name=STR (default: elderberry-cherry)
+ --training-file=STR (default: ./example-datasets/leo.txt)
+
+Other actions:
+ -h, --help Show the help
+
+$ ./main_cli.py predict -h
+Usage: ./main_cli.py predict [OPTIONS]
+
+Options:
+ --inference-text=STR (default: What is leo?)
+ --max-new-tokens=INT (default: 50)
+ --model-name=STR (default: ./lora-elderberry-cherry)
+ --repeat-penalty=INT (default: 1)
+ --temperature=FLOAT (default: 0.01)
+ --top-k=INT (default: 50)
+ --top-p=FLOAT (default: 0.3)
+
+Other actions:
+ -h, --help Show the help
+
+
+```
+
+### How to use?
+
+```
+$ ./main_cli.py train --training-file ./example-datasets/leo.txt
+
+$ ./main_cli.py predict --inference_text "C'est qui Paulivan"
+Un gros faignant
+```
+
 ## License
 
 MIT License

example-datasets/leo.txt

@@ -0,0 +1,8 @@
+Human: C'est qui Paulivan?
+
+Assistant: un gros faignant
+
+
+Human: C'est qui Sanson?
+
+Assistant: C'est le plus gros faignant