Update README.md

README.md

@@ -1,41 +1,44 @@
-## About
-
 # Problem Statement
-The objective of fine-tuning the CodeLlama model for the SQL dataset was to enhance its ability to generate accurate SQL queries based on given prompts. The initial model performance was suboptimal, prompting the need for fine-tuning to improve query generation accuracy.
-
-# Dataset and Model
-Dataset: ChrisHayduk/Llama-2-SQL-Dataset
-
-Model: CodeLlama, a pre-trained language model tailored for code generation tasks, was chosen as the base model for fine-tuning. The model's architecture and extensive pre-training on code-related tasks provided a strong foundation for this specialized fine-tuning.
 
-# Training Procedure
-The fine-tuning process involved several key steps and configurations to optimize model performance:
+The objective of fine-tuning the CodeLlama model for the SQL dataset was to enhance its ability to generate accurate SQL queries based on given prompts. The initial model performance was suboptimal, prompting the need for fine-tuning to improve query generation accuracy.
 
-Library and Tools: PEFT was utilized for the fine-tuning process, leveraging its capabilities to adjust model parameters and improve inference accuracy.
+---
 
-Quantization Configuration: The bitsandbytes quantization method was employed with the following configuration:
+# Dataset and Model
 
-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: True
-bnb_4bit_compute_dtype: float32
-This configuration aimed to optimize model performance by reducing memory usage and improving inference speed while maintaining acceptable accuracy levels for SQL query generation.
+- **Dataset**: [ChrisHayduk/Llama-2-SQL-Dataset](https://huggingface.co/ChrisHayduk/Llama-2-SQL-Dataset)
+- **Model**: CodeLlama, a pre-trained language model tailored for code generation tasks, was chosen as the base model for fine-tuning. The model's architecture and extensive pre-training on code-related tasks provided a strong foundation for this specialized fine-tuning.
 
-# Results and Evaluation
-The fine-tuning process aimed to address the initial performance issues observed in the model's ability to generate SQL queries accurately. Post-fine-tuning evaluation involved measuring the model's accuracy, speed of query generation, and overall efficiency in handling various query complexities.
+---
 
+# Training Procedure
 
-## Training procedure
+The fine-tuning process involved several key steps and configurations to optimize model performance:
 
+- **Library and Tools**: PEFT was utilized for the fine-tuning process, leveraging its capabilities to adjust model parameters and improve inference accuracy.
+  
+- **Quantization Configuration**: The `bitsandbytes` quantization method was employed with the following configuration:
+
+  ```yaml
+  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: True
+  bnb_4bit_compute_dtype: float32
+
+# Result
+  The fine-tuning process aimed to address the initial performance issues observed in the model's ability to generate SQL queries accurately. Post-fine-tuning evaluation involved measuring the model's accuracy, speed of query generation, and overall efficiency in handling various query complexities.
+
+```bash
 import torch
 from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
-#!pip install accelerate
+from peft import PeftModel
+
 base_model = "codellama/CodeLlama-7b-hf"
 model = AutoModelForCausalLM.from_pretrained(
     base_model,
@@ -44,9 +47,10 @@ model = AutoModelForCausalLM.from_pretrained(
     device_map="auto",
 )
 tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-hf")
-from peft import PeftModel
+
 output_dir = "model/output/path"
 model = PeftModel.from_pretrained(model, output_dir)
+
 eval_prompt = """You are a powerful text-to-SQL model. Your job is to answer questions about a database and explain your answer in detail. You are given a question and context regarding one or more tables.
 
 You must output the SQL query that answers the question.
@@ -66,7 +70,3 @@ model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda")
 model.eval()
 with torch.no_grad():
     print(tokenizer.decode(model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))
-    
-### Framework versions
-
-- PEFT 0.5.0