Create README.md

README.md

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+Apply Delta weights
+
+```python
+
+"""
+Usage:
+python3 apply_delta.py --base /path/to/model_weights/llama-13b --target stable-vicuna-13b --delta pvduy/stable-vicuna-13b-delta
+"""
+import argparse
+
+import torch
+from tqdm import tqdm
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+def apply_delta(base_model_path, target_model_path, delta_path):
+    print("Loading base model")
+    base = AutoModelForCausalLM.from_pretrained(
+        base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+
+    print("Loading delta")
+    delta = AutoModelForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+    delta_tokenizer = AutoTokenizer.from_pretrained(delta_path)
+
+    DEFAULT_PAD_TOKEN = "[PAD]"
+    base_tokenizer = AutoTokenizer.from_pretrained(base_model_path, use_fast=False)
+    num_new_tokens = base_tokenizer.add_special_tokens(dict(pad_token=DEFAULT_PAD_TOKEN))
+
+    base.resize_token_embeddings(len(base_tokenizer))
+    input_embeddings = base.get_input_embeddings().weight.data
+    output_embeddings = base.get_output_embeddings().weight.data
+    input_embeddings[-num_new_tokens:] = 0
+    output_embeddings[-num_new_tokens:] = 0
+
+    print("Applying delta")
+    for name, param in tqdm(base.state_dict().items(), desc="Applying delta"):
+        assert name in delta.state_dict()
+        param.data += delta.state_dict()[name]
+
+    print("Saving target model")
+    base.save_pretrained(target_model_path)
+    delta_tokenizer.save_pretrained(target_model_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base-model-path", type=str, required=True)
+    parser.add_argument("--target-model-path", type=str, required=True)
+    parser.add_argument("--delta-path", type=str, required=True)
+
+    args = parser.parse_args()
+
+    apply_delta(args.base_model_path, args.target_model_path, args.delta_path)
+```