Update README.md

README.md

@@ -1072,6 +1072,7 @@ configs:
 - [About](#about)
 - [Installation](#installation)
 - [Quick Start](#quick-start)
+- [Detailed Usage](#detailed-usage)
 - [Benchmark](#benchmark)
 - [Citation](#citation)
 
@@ -1084,11 +1085,13 @@ PLSemanticsBench is the first benchmark for evaluating LLMs as programming langu
 | ✨ **PredRule** | Predicts the ordered sequence of semantic rules needed to evaluate a program|
 | ✨ **PredTrace**| Predicts the step-by-step execution of a program |
 
+You must implement [BaseRunner](https://github.com/EngineeringSoftware/PLSemanticsBench/blob/main/src/plsemanticsbench/core/exps/base_experiment.py)(`_query` method) to evaluate your models. We provide two example implementations for OpenAI models ([GPTRunner](https://github.com/EngineeringSoftware/PLSemanticsBench/blob/main/src/plsemanticsbench/core/exps/gpt_experiment.py)) and Ollama models ([OllamaRunner](https://github.com/EngineeringSoftware/PLSemanticsBench/blob/main/src/plsemanticsbench/core/exps/ollama_experiment.py)).
+
 ## Installation
 
 ### System Requirements
 - Python 3.11 or higher
-- OpenAI API key (for running experiments)
+- OpenAI API key (for running experiments with OpenAI models)
 
 
 ### Step-by-Step Installation
@@ -1098,19 +1101,31 @@ conda env create -f env.yaml
 conda activate plsemanticsbench
 ```
 
-2. Set up your OpenAI API key:
+2. Set up your OpenAI API key (only for OpenAI models):
 ```bash
 export OPENAI_API_KEY='your-api-key-here'
 ```
 
-
 ## Quick Start
 
+We provide a bash script `quick` that:
+ 1. Sets up the `plsemanticsbench` conda environment.
+ 2. Pulls the `DeepSeek-R1 1.5B` model.
+ 3. Evaluates the `DeepSeek-R1 1.5B` model on the `PredState` task with `no-semantics` and `chain-of-thought` prompting on the `Human-Written` dataset.
+ 4. Prints the `accuracy` and `malformed-count` to screen.
+ 5. Creates `metrics-predstate-deepseek-r1:1.5b.json` that contains the evaluation result.
+ 
+```bash
+bash quick
+```
+
+## Detailed Usage
+
 ### Basic Example
 Here's a minimal example to get started:
 
 ```python
-from plsemanticsbench import GPTRunner, GPT_MODEL_ENUM
+from plsemanticsbench import GPTRunner
 from plsemanticsbench import ExperimentArgs, LLMEvaluator
 from plsemanticsbench import (
     PROMPT_STRATEGY,
@@ -1138,21 +1153,19 @@ exp_args = ExperimentArgs(
 )
                         
 # Run inference using the OpenAI API
-gpt_runner = GPTRunner(
-    gpt_model=GPT_MODEL_ENUM.O3_MINI,
-    args=exp_args,
-)
+gpt_runner = GPTRunner(args=exp_args)
 
-# If prediction file is provided, the predictions will be saved to the file
-predictions = gpt_runner.do_experiment()
-llm_eval = LLMEvaluator(exp_args)
+# Generation (generate LLM prediction on the predstate task)
+predictions = gpt_runner.do_experiment() # path to dump results can be provided
+
+# Evaluation (evaluate LLM prediction against ground-truth)
+llm_eval = LLMEvaluator(task=exp_args.task, semantics_type=exp_args.semantics_type)
 evaluation_result = llm_eval.evaluate_from_list(results=predictions, model_name=model_name)
 print(evaluation_result)
 ```
 
 ### Expected Output
 
-The evaluation results will look like:
 ```python
 {
     'accuracy': 1,
@@ -1161,6 +1174,7 @@ The evaluation results will look like:
 ```
 
 ## Benchmark
+
 You can load the dataset using the `datasets` library. Here is an example:
 ```python
 from datasets import load_dataset