feat: sft script can take model and dataset

.gitignore

@@ -161,6 +161,4 @@ cython_debug/
 #.idea/
 
 checkpoint_dir/
-results/
-llama-2-7b*/
 node_modules/

.vscode/settings.json

@@ -1,4 +1,4 @@
 {
-    "editor.formatOnSave": true,
-    "typescript.validate.enable": true
+    "editor.accessibilityPageSize": 12,
+    "editor.formatOnSave": true
 }

README.md

@@ -1,68 +1,68 @@
-# FinetuneLLMs (Work in Progress, Actively! 🔥)
-
-Finetune an LLM, within a few clicks!
-
-## 🔥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 on hand. 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.
-
-In a way, it helps provide an opportunity for everyone who has hardware available and wants to utilize it in the AI field.
-
-### [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)
-
-You need CUDA for now, but once llama.cpp is integrated, this will no longer be required.
-
-- For Linux
-
-  Install CUDA from [Nvidia installation guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)
-
-- For Windows (with Nvidia GPU)
-
-  Enable WSL2 on your machine.
-
-  Install CUDA from [Nvidia installation guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)
-
-## Dev Setup
-
-Setup frontend
-
-```
-// copy .env.example to .env
-cd frontend
-npm ci
-npm run dev
-// or yarn && yarn dev
-```
-
-Setup server
-
-```
-// copy .env.example to .env and .env.development
-cd server
-npx prisma migrate dev
-yarn
-yarn dev
-```
-
-Setup trainer
-
-```
-cd trainer
-pip install -r requirements.txt
-daphne trainer.asgi:application
-```
+# FinetuneLLMs (Work in Progress, Actively! 🔥)
+
+Finetune an LLM, within a few clicks!
+
+## 🔥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.
+
+In a way, it helps provide an opportunity for everyone who has hardware available and wants to utilize it in the AI field.
+
+### [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)
+
+You need CUDA for now, but once llama.cpp is integrated, this will no longer be required.
+
+- For Linux
+
+  Install CUDA from [Nvidia installation guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)
+
+- For Windows (with Nvidia GPU)
+
+  Enable WSL2 on your machine.
+
+  Install CUDA from [Nvidia installation guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)
+
+## Dev Setup
+
+Setup frontend
+
+```
+// copy .env.example to .env
+cd frontend
+npm ci
+npm run dev
+// or yarn && yarn dev
+```
+
+Setup server
+
+```
+// copy .env.example to .env and .env.development
+cd server
+npx prisma migrate dev
+yarn
+yarn dev
+```
+
+Setup trainer
+
+```
+cd trainer
+pip install -r requirements.txt
+daphne trainer.asgi:application
+```

frontend/.env.example

@@ -1,3 +1,3 @@
-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.
+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.