Update README.md

README.md

@@ -17,22 +17,32 @@ tags:
 SQL Generation model which is fine-tuned on the Mistral-7B-Instruct-v0.1.
 Inspired from https://huggingface.co/kanxxyc/Mistral-7B-SQLTuned
 
-## Training procedure
-
-
-The following `bitsandbytes` quantization config was used during training:
-- quant_method: bitsandbytes
-- load_in_8bit: False
-- load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: False
-- bnb_4bit_compute_dtype: float16
-
-### Framework versions
-
-
-- PEFT 0.6.0.dev0
+### Code 
+```py
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+peft_model_id = "AhmedSSoliman/Mistral-Instruct-SQL-Generation"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, trust_remote_code=True, return_dict=True, load_in_4bit=True, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+def predict_SQL(table, question):
+    pipe = pipeline('text-generation', model = base_model, tokenizer = tokenizer)
+    prompt = f"[INST] Write SQL query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: {table} Question: {question} [/INST] Here is the SQL query to answer to the question: {question}: ``` "
+    #prompt = f"### Schema: {table} ### Question: {question} # "
+    ans = pipe(prompt, max_new_tokens=200)
+    generatedSql = ans[0]['generated_text'].split('```')[2]
+    return generatedSql
+
+
+table = "CREATE TABLE Employee (name VARCHAR, salary INTEGER);"
+question = 'Show names for all employees with salary more than the average.'
+
+generatedSql=predict_SQL(table, question)
+print(generatedSql)
+
+```