Refactoring entrypoints

.gitignore

@@ -166,15 +166,8 @@ cython_debug/
 __pycache__/
 *.egg-info/
 *.egg
-
-data/*
-!data/AlpacaDataCleaned/
-template/*
-!data/data_demo.json
-!data/dummy_data.json
-!template/test_data_demo.json
-!template/template_demo.json
-data_train.json
+mlora.json
+mlora_train_*.json
 
 # macOS junk files
 .DS_Store

Dockerfile

@@ -45,7 +45,6 @@ RUN . ~/.bashrc \
     && cd /mLoRA \
     && pyenv virtualenv $PYTHON_VERSION mlora \
     && pyenv local mlora \
-    && pip install torch==2.3.1 \
     && pip install -r ./requirements.txt
 
 WORKDIR /mLoRA

README.md

@@ -121,13 +121,15 @@ You can conveniently utilize m-LoRA via `launch.py`. The following example demon
 
 ```bash
 # Generating configuration
-python launch.py gen --template lora --tasks ./data/dummy_data.json
+python launch.py gen --template lora --tasks ./tests/dummy_data.json
+
 # Running the training task
 python launch.py run --base_model TinyLlama/TinyLlama_v1.1
+
 # Try with gradio web ui
 python inference.py \
   --base_model TinyLlama/TinyLlama_v1.1 \
-  --template ./template/alpaca.json \
+  --template alpaca \
   --lora_weights ./casual_0
 ```
 
@@ -140,15 +142,21 @@ python launch.py help
 ## m-LoRA
 
 The `mlora.py` code is a starting point for finetuning on various datasets.
+
 Basic command for finetuning a baseline model on the [Alpaca Cleaned](https://github.com/gururise/AlpacaDataCleaned) dataset:
 ```bash
+# Generating configuration
+python launch.py gen \
+  --template lora \
+  --tasks yahma/alpaca-cleaned
+
 python mlora.py \
   --base_model meta-llama/Llama-2-7b-hf \
-  --config ./config/alpaca.json \
+  --config mlora.json \
   --bf16
 ```
 
-You can check the template finetune configuration in [template](./template/) folder.
+You can check the template finetune configuration in [templates](./templates/) folder.
 
 For further detailed usage information, please use `--help` option:
 ```bash