add slerp in qwen

train/qwen/merge_model.py

@@ -33,8 +33,8 @@ def main():
     file_path = os.path.dirname(__file__)
 
     base_model_path = file_path + "/../../data/models/Qwen3-Embedding-0.6B/model"
-    peft_model_path = file_path + "/output/v23-20251214-111804/checkpoint-3632"
-    save_path = file_path + "/output/v23-20251214-111804/merged_checkpoint-3632"
+    peft_model_path = file_path + "/output/v23-20251214-111804/slerp-checkpoint"
+    save_path = file_path + "/output/v23-20251214-111804/merged_checkpoint-slerp"
     merge(base_model_path, peft_model_path, save_path)
 
     items = ["1_Pooling", "config_sentence_transformers.json", "merges.txt", "modules.json", "README.md", "tokenizer_config.json", "tokenizer.json", 

train/qwen/slerp_merge.py

@@ -0,0 +1,114 @@
+import json
+import numpy as np
+import os
+from peft import PeftModel
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import shutil
+from safetensors.torch import load_file
+from safetensors.torch import save_file
+
+os.environ["PEFT_BOFT_FORCE_CPU"] = "1"
+
+
+
+def slerp(t, v0, v1, eps=1e-8):
+    v0_norm = v0 / (v0.norm() + eps)
+    v1_norm = v1 / (v1.norm() + eps)
+
+    dot = (v0_norm * v1_norm).sum()
+    dot = torch.clamp(dot, -1.0, 1.0)
+
+    if dot > 0.9995:
+        # fallback to linear interpolation
+        return (1 - t) * v0 + t * v1
+
+    theta_0 = torch.acos(dot)
+    sin_theta_0 = torch.sin(theta_0)
+
+    theta_t = theta_0 * t
+    sin_theta_t = torch.sin(theta_t)
+
+    s0 = torch.sin(theta_0 - theta_t) / sin_theta_0
+    s1 = sin_theta_t / sin_theta_0
+
+    return s0 * v0 + s1 * v1
+
+
+def load_lora(path):
+    return load_file(path)
+
+def merge_loras_slerp(lora_paths, weights=None):
+    loras = [load_lora(p) for p in lora_paths]
+
+    if weights is None:
+        weights = [1 / len(loras)] * len(loras)
+
+    merged = {}
+
+    keys = loras[0].keys()
+
+    for k in keys:
+        tensors = [l[k].float() for l in loras]
+
+        # iterative slerp
+        out = tensors[0]
+        acc_weight = weights[0]
+
+        for i in range(1, len(tensors)):
+            t = weights[i] / (acc_weight + weights[i])
+            out = slerp(t, out, tensors[i])
+            acc_weight += weights[i]
+
+        merged[k] = out.to(tensors[0].dtype)
+
+    return merged
+
+
+def merge(base_model_path, peft_model_path, save_path):
+    base_model = AutoModelForCausalLM.from_pretrained(base_model_path, torch_dtype="bfloat16")
+    ft_model = PeftModel.from_pretrained(base_model, peft_model_path)
+    ft_model = ft_model.merge_and_unload()
+    ft_model.save_pretrained(save_path)
+
+def copy_selected_items(src_path, dst_path, items):
+    os.makedirs(dst_path, exist_ok=True)
+
+    for item in items:
+        source_item = os.path.join(src_path, item)
+        dest_item = os.path.join(dst_path, item)
+
+        if not os.path.exists(source_item):
+            print(f"⚠ {item} در مسیر مبدا پیدا نشد!")
+            continue
+
+        if os.path.isdir(source_item):
+            shutil.copytree(source_item, dest_item, dirs_exist_ok=True)
+        elif os.path.isfile(source_item):
+            shutil.copy2(source_item, dest_item)
+
+def main():
+    file_path = os.path.dirname(__file__)
+
+    # base_model_path = file_path + "/../../data/models/Qwen3-Embedding-0.6B/model"
+    peft_model_path = [
+        file_path + "/output/v23-20251214-111804/checkpoint-3632",
+        file_path + "/output/v23-20251214-111804/checkpoint-3000",
+        file_path + "/output/v23-20251214-111804/checkpoint-2000",
+    ]
+    save_path = file_path + "/output/v23-20251214-111804/slerp-checkpoint"
+
+
+    merged_lora = merge_loras_slerp([peft_model_path[i] + "/adapter_model.safetensors" for i in range(len(peft_model_path))])
+
+    os.makedirs(save_path, exist_ok=True)
+    
+    save_file(merged_lora, save_path + "/adapter_model.safetensors")
+    # merge(base_model_path, peft_model_path, save_path)
+
+    items = ["adapter_config.json", "additional_config.json"]
+    copy_selected_items(peft_model_path[0], save_path, items)
+
+
+if __name__ == "__main__":
+    main()