add

models/LLaVA/build/lib/llava/data/alpaca-converter.py

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+import argparse
+import json
+import pathlib
+
+# Prompt from stanford alpaca's training script
+PROMPT_DICT = {
+    "prompt_input": (
+        "Below is an instruction that describes a task, paired with an input that provides further context. "
+        "Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
+    ),
+    "prompt_no_input": (
+        "Below is an instruction that describes a task. "
+        "Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Response:"
+    ),
+}
+
+
+def main(args):
+    data_path = pathlib.Path(args.data_path)
+    with data_path.open() as f:
+        data = json.load(f)
+
+    prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"]
+    sources = [
+        prompt_input.format_map(example) if example.get("input", "") != "" else prompt_no_input.format_map(example)
+        for example in data
+    ]
+    targets = [example['output'] for example in data]
+
+    new_data = []
+    cnt = 1
+    for s, t in zip(sources, targets):
+        new_data.append({
+            'id': str(cnt),
+            'conversations': [
+                {
+                    'from': 'human',
+                    'value': s,
+                },
+                {
+                    'from': 'gpt',
+                    'value': t,
+                }
+            ]
+        })
+        cnt += 1
+
+    json.dump(new_data, open(args.output_path, 'w'), indent=2)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_path', type=str, default='alpaca-data.json')
+    parser.add_argument('--output_path', type=str, default='alpaca-data-conversation.json')
+    args = parser.parse_args()
+    main(args)
+