Create README.md

README.md

@@ -0,0 +1,52 @@
+# Plan_Q-RAG
+
+## Setup Rent GPU
+Git Clone to all the required data
+```bash
+
+git clone https://github.com/griver/Q-RAG.git
+cd Q-RAG
+#Only need when you don't have your self-trained model
+git clone https://huggingface.co/Q-RAG/qrag-ft-e5-on-hotpotqa
+```
+Environment Setup
+```bash
+# Setup venv
+conda create -n qrag python=3.12 -y
+conda activate qrag
+
+python -m pip install -U pip wheel
+pip install vllm  # pulls compatible PyTorch, Transformers, Triton, etc.
+pip install hydra-core tensorboard rotary-embedding-torch pandas nltk sortedcontainers accelerate datasets
+
+# Check environment
+python -c "from rl.agents.pqn import PQNActor; print('✅ Q-RAG installed successfully')"
+
+```
+
+```bash
+cd ..
+
+python train_q_rag_logt.py \
+   envs=hotpotqa \
+   algo=pqn_e5_hotpotqa \
+   envs.data_path="/workspace/datasets/hotpotqa" \
+   steps_count=10000 \
+   batch_size=12 \
+   accumulate_grads=8 \
+   eval_interval=50 \ #original 100
+   envs_parallel=1 \
+   max_action_length=220
+
+python train_q_rag.py \
+   envs=hotpotqa \
+   algo=pqn_e5_hotpotqa \
+   envs.data_path="/workspace/datasets/hotpotqa" \
+   steps_count=10000 \
+   batch_size=12 \
+   accumulate_grads=8 \
+   eval_interval=100\
+   envs_parallel=1 \
+   max_action_length=220
+
+```