Added running instruction

README.md

@@ -1,3 +1,61 @@
 ---
 license: mit
 ---
+
+# SQLMaster
+A minimum of 8 GB VRAM is required.
+
+## Colab Example
+https://colab.research.google.com/drive/1kMv2nw4gqsQLkLGUUEAI31XOD_7BykDj?usp=sharing
+
+## Install Prerequisite
+```bash
+!pip install peft
+!pip install transformers
+!pip install bitsandbytes
+!pip install accelerate
+```
+
+## Login Using Huggingface Token
+```bash
+# You need a huggingface token that can access llama2
+from huggingface_hub import notebook_login
+notebook_login()
+```
+
+## Download Model
+```python
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+peft_model_id = "Danjie/SQLMaster"
+config = PeftConfig.from_pretrained(peft_model_id)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
+model.resize_token_embeddings(len(tokenizer) + 1)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+```
+
+## Inference
+```python
+def create_sql_query(question: str, context: str) -> str:
+    input = "Question: " + question + "\nContext:" + context + "\nAnswer"
+    
+    # Encode and move tensor into cuda if applicable.
+    encoded_input = tokenizer(input, return_tensors='pt')
+    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}
+
+    output = model.generate(**encoded_input, max_new_tokens=256)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    response = response[len(input):]
+    return response
+```
+
+## Example
+```python
+create_sql_query("What is the highest age of users with name Danjie", "CREATE TABLE user (age INTEGER, name STRING)")
+```