Merge pull request stochasticai#147 from stochasticai/glenn/readme

docs: update readme

README.md

@@ -2,25 +2,8 @@
   <img src=".github/stochastic_logo_light.svg#gh-light-mode-only" width="250" alt="Stochastic.ai"/>
   <img src=".github/stochastic_logo_dark.svg#gh-dark-mode-only" width="250" alt="Stochastic.ai"/>
 </p>
-<h3 align="center">Build and control your own LLMs</h3>
+<h3 align="center">Build, customize and control your own personal LLMs</h3>
 
-___
-
-`xturing` provides fast, efficient and simple fine-tuning of LLMs, such as LLaMA, GPT-J, GPT-2,
-OPT, Cerebras-GPT, Galactica, and more.
-By providing an easy-to-use interface for personalizing LLMs to your own data and application,
-xTuring makes it simple to build and control LLMs.
-The entire process can be done inside your computer or in your private cloud,
-ensuring data privacy and security.
-
-With `xturing` you can,
-- Ingest data from different sources and preprocess them to a format LLMs can understand
-- Scale from single to multiple GPUs for faster fine-tuning
-- Leverage memory-efficient techniques (i.e. INT4, LoRA fine-tuning) to reduce your hardware costs by up to 90% of the time
-- Explore different fine-tuning methods and benchmark them to find the best performing model
-- Evaluate fine-tuned models on well-defined metrics for in-depth analysis
-
-<br>
 <p align="center">
   <a href="https://pypi.org/project/xturing/">
     <img src="https://img.shields.io/pypi/v/xturing?style=for-the-badge" />
@@ -32,12 +15,27 @@ With `xturing` you can,
     <img src="https://img.shields.io/badge/Chat-FFFFFF?logo=discord&style=for-the-badge"/>
   </a>
 </p>
+<br>
+
+___
+
+`xturing` provides fast, efficient and simple fine-tuning of LLMs, such as LLaMA, GPT-J, Galactica, and more.
+By providing an easy-to-use interface for fine-tuning LLMs to your own data and application, xTuring makes it
+simple to build, customize and control LLMs. The entire process can be done inside your computer or in your
+private cloud, ensuring data privacy and security.
+
+With `xturing` you can,
+- Ingest data from different sources and preprocess them to a format LLMs can understand
+- Scale from single to multiple GPUs for faster fine-tuning
+- Leverage memory-efficient methods (i.e. INT4, LoRA fine-tuning) to reduce hardware costs by up to 90%
+- Explore different fine-tuning methods and benchmark them to find the best performing model
+- Evaluate fine-tuned models on well-defined metrics for in-depth analysis
 
 <br>
 
-## 🌟 _New feature_ - INT4 fine-tuning with LLaMA LoRA
+## 🌟 INT4 fine-tuning with LLaMA LoRA
 
-We are excited to announce the latest enhancement to our `xTuring` library: INT4 fine-tuning demo. With this update, you can fine-tune LLMs like LLaMA with LoRA architecture in INT4 precision with less than `6GB` of VRAM. This breakthrough significantly reduces memory requirements and accelerates the fine-tuning process, allowing you to achieve state-of-the-art performance with less computational resources.
+We are excited to announce the latest enhancement to our `xTuring` library: INT4 fine-tuning demo. With this update, you can fine-tune LLMs like LLaMA with LoRA architecture in INT4 precision with less than `6 GB` of VRAM. This breakthrough significantly reduces memory requirements and accelerates the fine-tuning process, allowing you to achieve state-of-the-art performance with less computational resources.
 
 More information about INT4 fine-tuning and benchmarks can be found in the [INT4 README](examples/int4_finetuning/README.md).
 
@@ -51,6 +49,8 @@ You can check out the [LLaMA INT4 fine-tuning example](examples/int4_finetuning/
 ## UI playground
 <img src=".github/ui-playground2.gif" width="100%" style="margin: 0 1%;"/>
 
+<br>
+
 ## ⚙️ Installation
 ```bash
 pip install xturing
@@ -82,23 +82,17 @@ You can find the data folder [here](examples/llama/alpaca_data).
 
 <br>
 
-
 ## 📚 Tutorials
 - [Preparing your dataset](examples/llama/preparing_your_dataset.py)
 - [Cerebras-GPT fine-tuning with LoRA and INT8](examples/cerebras/cerebras_lora_int8.ipynb) &ensp; [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eKq3oF7dnK8KuIfsTE70Gvvniwr1O9D0?usp=sharing)
 - [Cerebras-GPT fine-tuning with LoRA](examples/cerebras/cerebras_lora.ipynb) &ensp; [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1VjqQhstm5pT4EjPjx4Je7b3W2X1V3vDo?usp=sharing)
-- [LLaMA with LoRA and INT8](examples/llama/llama_lora_int8.py) &ensp; [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SQUXq1AMZPSLD4mk3A3swUIc6Y2dclme?usp=sharing)
-- [LLaMA with LoRA](examples/llama/llama_lora.py)
-- [LLaMA easy fine-tuning](examples/llama/llama.py)
-- [GPT-J efficient fine-tuning with LoRA and INT8](examples/gptj/gptj_lora_int8.py) &ensp; [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hB_8s1V9K4IzifmlmN2AovGEJzTB1c7e?usp=sharing)
+- [LLaMA fine-tuning with LoRA and INT8](examples/llama/llama_lora_int8.py) &ensp; [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SQUXq1AMZPSLD4mk3A3swUIc6Y2dclme?usp=sharing)
+- [LLaMA fine-tuning with LoRA](examples/llama/llama_lora.py)
+- [LLaMA fine-tuning](examples/llama/llama.py)
+- [GPT-J fine-tuning with LoRA and INT8](examples/gptj/gptj_lora_int8.py) &ensp; [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hB_8s1V9K4IzifmlmN2AovGEJzTB1c7e?usp=sharing)
 - [GPT-J fine-tuning with LoRA](examples/gptj/gptj_lora.py)
-- [Galactica fine-tuning with LoRA and INT8](examples/galactica/galactica_lora_int8.py)
-- [Galactica fine-tuning with LoRA](examples/galactica/galactica_lora.py)
-- [OPT fine-tuning with LoRA and INT8](examples/opt/opt_lora_int8.py)
-- [OPT fine-tuning with LoRA](examples/opt/opt_lora.py)
 - [GPT-2 fine-tuning with LoRA](examples/gpt2/gpt2_lora.py) &ensp; [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1Sh-ocNpKn9pS7jv6oBb_Q8DitFyj1avL/view?usp=sharing)
 
-
 <br>
 
 ## 📊 Performance
@@ -124,7 +118,7 @@ Fine-tuning parameters:
 | CPU | 190 GB  | 10.2 GB | 14.9 GB |
 | Time per epoch | 21 hours  | 20 mins | 20 mins |
 
-Please submit your performance results on other GPUs.
+Contribute to this by submitting your performance results on other GPUs by creating an issue with your hardware specifications, memory consumption and time per epoch.
 
 <br>
 
@@ -147,9 +141,9 @@ model = BaseModel.load("x/distilgpt2_lora_finetuned_alpaca")
 ## 📈 Roadmap
 - [x] Support for LLaMA, GPT-J, GPT-2, OPT, Cerebras-GPT, Galactica and Bloom models
 - [x] Dataset generation using self-instruction
-- [x] 2x more memory-efficient fine-tuning vs LoRA and unsupervised fine-tuning
+- [x] Low-precision LoRA fine-tuning and unsupervised fine-tuning
 - [x] INT8 low-precision fine-tuning support
-- [x] Supports OpenAI, Cohere and AI21 Studio model APIs for dataset generation
+- [x] OpenAI, Cohere and AI21 Studio model APIs for dataset generation
 - [x] Added fine-tuned checkpoints for some models to the hub
 - [x] INT4 LLaMA LoRA fine-tuning demo
 - [ ] Evaluation of LLM models