diff --git a/README.md b/README.md
index 252f4d1..dc879ab 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,11 @@
-# Run LLM chat in realtime on an 8GB NVIDIA GPU
+# Alpaca Lora 4bit
+Made some adjust for the code in peft and gptq for llama, and make it possible for lora finetuning with a 4 bits base model. The same adjustment can be made for 2, 3 and 8 bits.
 
-## Dockerfile for alpaca_lora_4bit
-This repo is a Dockerfile wrapper for https://github.com/johnsmith0031/alpaca_lora_4bit
-
-## Use
-Can run real-time LLM chat using alpaca on a 8GB NVIDIA/CUDA GPU (ie 3070 Ti mobile)
-
-## Requirements
-- Linux
-- Docker
-- NVIDIA GPU with driver version that supports CUDA 11.7+ (e.g. 525)
-
-## Installation
+## Quick start for running the chat UI
 
 ```
 git clone https://github.com/andybarry/alpaca_lora_4bit_docker.git
-docker build -t alpaca_lora_4bit .
+DOCCKER_BUILDKIT=1 docker build -t alpaca_lora_4bit .
 docker run --gpus=all -p 7860:7860 alpaca_lora_4bit
 ```
 Point your browser to http://localhost:7860
@@ -25,12 +15,88 @@ It's fast on a 3070 Ti mobile.  Uses 5-6 GB of GPU RAM.
 
 ![](alpaca_lora_4bit_penguin_fact.gif)
 
-The model isn't all that good, sometimes it goes crazy.  But hey, as I always say, "when 4-bits _you reach_ look as good, you will not."
+# Development
+* Install Manual by s4rduk4r: https://github.com/s4rduk4r/alpaca_lora_4bit_readme/blob/main/README.md (**NOTE:** don't use the install script, use the requirements.txt instead.)
+* Also Remember to create a venv if you do not want the packages be overwritten.
 
+# Update Logs
+* Resolved numerically unstable issue
+* Reconstruct fp16 matrix from 4bit data and call torch.matmul largely increased the inference speed.
+* Added install script for windows and linux.
+* Added Gradient Checkpointing. Now It can finetune 30b model 4bit on a single GPU with 24G VRAM with Gradient Checkpointing enabled. (finetune.py updated) (but would reduce training speed, so if having enough VRAM this option is not needed)
+* Added install manual by s4rduk4r
+* Added pip install support by sterlind, preparing to merge changes upstream
+* Added V2 model support (with groupsize, both inference + finetune)
+* Added some options on finetune: set default to use eos_token instead of padding, add resume_checkpoint to continue training
+* Added offload support. load_llama_model_4bit_low_ram_and_offload_to_cpu function can be used.
+* Added monkey patch for text generation webui for fixing initial eos token issue.
+* Added Flash attention support. (Use --flash-attention)
+* Added Triton backend to support model using groupsize and act-order. (Use --backend=triton)
 
-## References
+# Requirements
+gptq-for-llama <br>
+peft<br>
+The specific version is inside requirements.txt<br>
 
-- https://github.com/johnsmith0031/alpaca_lora_4bit
-- https://github.com/s4rduk4r/alpaca_lora_4bit_readme/blob/main/README.md
-- https://github.com/tloen/alpaca-lora
+# Install
+~copy files from GPTQ-for-LLaMa into GPTQ-for-LLaMa path and re-compile cuda extension~<br>
+~copy files from peft/tuners/lora.py to peft path, replace it~<br>
 
+**NOTE:** Install scripts are no longer needed! requirements.txt now pulls from forks with the necessary patches.
+
+```
+pip install -r requirements.txt
+```
+
+# Finetune
+~The same finetune script from https://github.com/tloen/alpaca-lora can be used.~<br>
+
+After installation, this script can be used:
+GPTQv1:
+
+```
+python finetune.py
+```
+or
+```
+GPTQ_VERSION=1 python finetune.py
+```
+
+GPTQv2:
+```
+GPTQ_VERSION=2 python finetune.py
+```
+
+# Inference
+
+After installation, this script can be used:
+
+```
+python inference.py
+```
+
+# Text Generation Webui Monkey Patch
+
+Clone the latest version of text generation webui and copy all the files into ./text-generation-webui/
+```
+git clone https://github.com/oobabooga/text-generation-webui.git
+```
+
+Open server.py and insert a line at the beginning
+```
+import custom_monkey_patch # apply monkey patch
+import gc
+import io
+...
+```
+
+Use the command to run
+
+```
+python server.py
+```
+
+# Flash Attention
+
+It seems that we can apply a monkey patch for llama model. To use it, simply download the file from [MonkeyPatch](https://github.com/lm-sys/FastChat/blob/daa9c11080ceced2bd52c3e0027e4f64b1512683/fastchat/train/llama_flash_attn_monkey_patch.py). And also, flash-attention is needed, and currently do not support pytorch 2.0.
+Just add --flash-attention to use it for finetuning.