wip: ui update and pipelining

README.md

@@ -2,6 +2,16 @@
 
 Finetune an LLM, within a few clicks!
 
+<p align="center">
+  <a href="https://discord.gg/kghtMX7v" target="_blank">
+    <img src="https://img.shields.io/badge/FinetuneLLMs-chat_with_us-blue" alt="Discord">
+  </a>
+  <a href="https://github.com/jazelly/FinetuneLLMs/blob/main/LICENSE" target="_blank">
+      <img src="https://img.shields.io/static/v1?label=license&message=MIT&color=white" alt="License">
+  </a>
+
+</p>
+
 ## 🔥Goal & Roadmap🔥
 
 The main objective of this project is to lower the barrier to training large language models, especially for startup companies that have hardware in hands. With this project, it should be easy for a company to start experimenting with LLM training within a basic setup on servers with GPU/CPU resources.
@@ -10,17 +20,6 @@ In a way, it helps provide an opportunity for everyone who has hardware availabl
 
 ### [Roadmap](https://github.com/users/jazelly/projects/1/views/1)
 
-## Supported finetuning techniques
-
-| Model \ Method | SFT | DPO | ORPO | KTO | PRO |
-| -------------- | --- | --- | ---- | --- | --- |
-| llama 2        | ✅  | ❌  | ❌   | ❌  | ❌  |
-| llama 3        | ✅  | ❌  | ✅   | ❌  | ❌  |
-| gguf           | ✅  | ❌  | ❌   | ❌  | ❌  |
-| phi-3          | ✅  | ❌  | ❌   | ❌  | ❌  |
-| Mistral        | ✅  | ✅  | ❌   | ❌  | ❌  |
-| ...            | ?   | ?   | ?    | ?   | ?   |
-
 ## General Setup
 
 This repo provides 3 modules, `frontend `(react), `server` (nodejs), and `trainer` (python django)
@@ -39,30 +38,79 @@ You need CUDA for now, but once llama.cpp is integrated, this will no longer be
 
 ## Dev Setup
 
-Setup frontend
+### Setup frontend
 
 ```
-// copy .env.example to .env
 cd frontend
+// copy .env.example to .env
 npm ci
 npm run dev
 // or yarn && yarn dev
 ```
 
-Setup server
+### Setup server
 
 ```
-// copy .env.example to .env and .env.development
 cd server
+// copy .env.example to .env and .env.development
 npx prisma migrate dev
-yarn
-yarn dev
+npm i
+npm run dev
+```
+
+### Setup trainer
+
+#### Manual installation using Conda
+
+Recommended if you have some experience with the command-line.
+
+##### 0. Install Conda
+
+https://docs.conda.io/en/latest/miniconda.html
+
+On Linux or WSL, it can be automatically installed with these two commands ([source](https://educe-ubc.github.io/conda.html)):
+
+```
+curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
+bash Miniconda3.sh
+```
+
+##### 1. Create a new conda environment
+
+```
+conda create -n fllms python=3.11
+conda activate fllms
+```
+
+##### 2. Install Pytorch
+
+| System      | GPU      | Command                                                                                                                |
+| ----------- | -------- | ---------------------------------------------------------------------------------------------------------------------- |
+| Linux/WSL   | NVIDIA   | `pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121`   |
+| Linux/WSL   | CPU only | `pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cpu`     |
+| Linux       | AMD      | `pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/rocm5.6` |
+| MacOS + MPS | Any      | `pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1`                                                      |
+| Windows     | NVIDIA   | `pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121`   |
+| Windows     | CPU only | `pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1`                                                      |
+
+The up-to-date commands can be found here: https://pytorch.org/get-started/locally/.
+
+For NVIDIA, you also need to install the CUDA runtime libraries:
+
+```
+conda install -y -c "nvidia/label/cuda-12.1.1" cuda-runtime
+```
+
+If you need `nvcc` to compile some library manually, replace the command above with
+
+```
+conda install -y -c "nvidia/label/cuda-12.1.1" cuda
 ```
 
-Setup trainer
+##### 3. Install dependencies
 
 ```
 cd trainer
 pip install -r requirements.txt
-daphne trainer.asgi:application
+python watch.py
 ```

frontend/.env.example

@@ -1,3 +1,8 @@
+env='Development'
 VITE_API_BASE='http://localhost:3001/api' # Use this URL when developing locally
 # VITE_API_BASE="https://$CODESPACE_NAME-3001.$GITHUB_CODESPACES_PORT_FORWARDING_DOMAIN/api" # for Github Codespaces
 # VITE_API_BASE='/api' # Use this URL deploying on non-localhost address OR in docker.
+
+TRAINER_WS_URL_BASE='ws://localhost:8000/'
+
+TRAINER_API_BASE='http://localhost:8000/'

frontend/.prettierrc

@@ -0,0 +1,17 @@
+{
+    "printWidth": 80,       
+    "tabWidth": 2,           
+    "useTabs": false,    
+    "semi": true,         
+    "singleQuote": true,   
+    "quoteProps": "as-needed",
+    "jsxSingleQuote": false,     
+    "trailingComma": "es5",      
+    "bracketSpacing": true,      
+    "jsxBracketSameLine": false, 
+    "arrowParens": "always",     
+    "proseWrap": "preserve",     
+    "htmlWhitespaceSensitivity": "css",
+    "endOfLine": "lf", 
+    "embeddedLanguageFormatting": "auto"
+  }

frontend/.vscode/settings.json

@@ -1,3 +1,4 @@
 {
-    "editor.formatOnSave": true
+    "editor.formatOnSave": true,
+    "editor.rulers": [80]
 }