update model card

README.md

@@ -20,3 +20,49 @@ base_model: unsloth/llama-3-8b-bnb-4bit
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+
+## Usage
+
+
+```python
+from unsloth import FastLanguageModel
+from unsloth import FastLanguageModel
+import torch
+max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "surajgorai/llama_3_8b_text_to_sql_model", # YOUR MODEL YOU USED FOR TRAINING
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+)
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+prompt = """You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.
+
+You must output the SQL query that answers the question.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+# alpaca_prompt = You MUST copy from above!
+inputs = tokenizer(
+[
+    prompt.format(
+        'Name the result/games for 54741', # instruction
+        'CREATE TABLE table_21436373_11 (result_games VARCHAR, attendance VARCHAR)', # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
+tokenizer.batch_decode(outputs)
+```