Remove llava

.vscode/launch.json

@@ -60,79 +60,5 @@
                 "LOGLEVEL": "DEBUG",
             },
         },
-        {
-            "name": "Python: Eval - MathVista - (LLaVA) evaluation.generate_response",
-            "type": "debugpy",
-            "request": "launch",
-            "module": "evaluation.generate_response",
-            "justMyCode": true,
-            "args": [
-                "--conv-mode",
-                "vicuna_v1",
-                "--max_num_problems",
-                "100",
-                "--output_dir",
-                "_results/eval/mathvista/llava/debug",
-                "--output_file",
-                "llava-v1.5-7b.json",
-                "--model_path",
-                "liuhaotian/llava-v1.5-7b",
-                // TODO: Find out why loading from folder during debug not work?
-                // "/mnt/mattmprojects/exp/projects/mattm-projectwillow/amlt-results/7293878818.78845-fe8da8ef-7f38-4b40-b2e1-3259c7bf7a3e/llava/checkpoints/llava-vicuna-7b-v1.5-finetune",
-                "--save_every",
-                "1",
-            ],
-            "envFile": "${workspaceFolder}/.env",
-            "env": {
-                "CUDA_VISIBLE_DEVICES": "0",
-                "LOGLEVEL": "DEBUG",
-            },
-        },
-        {
-            "name": "Python: Eval - MathVista - (LLaVA) evaluation.extract_answer",
-            "type": "debugpy",
-            "request": "launch",
-            "module": "evaluation.extract_answer",
-            "justMyCode": true,
-            "args": [
-                "--max_num_problems",
-                "100",
-                "--results_file_path",
-                "_results/eval/mathvista/llava/debug/llava-v1.5-7b.json",
-                // "${workspaceFolder}/_results/eval/mathvista/20240213_223832/llava-v1.5-7b.json",
-                // "${workspaceFolder}/_results/eval/mathvista/debug/output_gpt4_2shot_solution_use_caption_ocr.json",
-            ],
-            "envFile": "${workspaceFolder}/.env",
-            "env": {
-                "CUDA_VISIBLE_DEVICES": "0",
-                "LOGLEVEL": "DEBUG",
-            },
-        },
-        {
-            "name": "Python: Eval - MathVista - (LLaVA) evaluation.calculate_score",
-            "type": "debugpy",
-            "request": "launch",
-            "module": "evaluation.calculate_score",
-            "justMyCode": true,
-            "args": [
-                "--output_dir",
-                // All Empty Responses
-                // "_results/eval/mathvista/20240214_210722",
-                // Legitimate Responses
-                // "_results/eval/mathvista/20240214_220834",
-                "_results/eval/mathvista/20240215_204602",
-                "--output_file",
-                "llava-v1.5-7b.json",
-                // "llava-v1.5-7b_false_positives.json",
-                "--score_file",
-                "llava-v1.5-7b_metrics.json",
-                "--ignore_empty_extractions",
-            ],
-            "envFile": "${workspaceFolder}/.env",
-            "env": {
-                "CUDA_VISIBLE_DEVICES": "0",
-                "LOGLEVEL": "DEBUG",
-            },
-        },
     ]
 }

evaluation/generate_response.py

@@ -146,19 +146,11 @@ def main():
 
     # If we were given a custom model path, load that model, otherwise use a remote service model
     if args.model_path:
-        from models import llava
+        # from models import llava
 
         logging.info(f"Loading model from {args.model_path}...")
-        model = llava.Llava_Model(
-            model_path=args.model_path,
-            model_base=args.model_base,
-            conv_mode=args.conv_mode,
-            temperature=args.temperature,
-            top_p=args.top_p,
-            num_beams=args.num_beams,
-            max_new_tokens=args.max_new_tokens,
-            seed_value=42,
-        )
+        # TODO: Add support for local models
+        raise NotImplementedError("Local models are not yet supported.")
     else:
         model_name = args.azure_openai_model if args.azure_openai_model else args.model
         logging.info(f"Loading {model_name}...")