Initial upload of JiRackTernary 70B converted weights

JiRackTernary1b.pt

@@ -1,3 +1,3 @@
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 size 5995037658

chat_jirack_ternary1b_safetensors.py

@@ -1,8 +1,3 @@
-# ==============================================================================
-# COPYRIGHT (C) 2026 KONSTANTIN VLADIMIROVICH GRABKO. ALL RIGHTS RESERVED.
-# PATENT PENDING | CMS MANHATTAN JIRACK TECHNOLOGY
-# ==============================================================================
-
 import torch
 import torch.nn.functional as F
 from transformers import AutoTokenizer

convert_to_safetensors.py

@@ -0,0 +1,34 @@
+import torch
+import os
+from safetensors.torch import save_file
+
+CHECKPOINT_PATH = "JiRackTernary1b.pt"
+OUTPUT_PATH = "model.safetensors"
+
+def convert():
+    print(f"📦 Loading tensors from: {CHECKPOINT_PATH}")
+    checkpoint = torch.load(CHECKPOINT_PATH, map_location="cpu")
+
+    # 1. Extract only the model weights dictionary
+    if "model_state_dict" in checkpoint:
+        state_dict = checkpoint["model_state_dict"]
+    else:
+        state_dict = checkpoint
+
+    # 2. Filter: keep ONLY tensors (weights) and remove metadata
+    clean_sd = {}
+    for k, v in state_dict.items():
+        if isinstance(v, torch.Tensor):
+            # Remove prefixes if they exist
+            new_key = k.replace("_orig_mod.", "").replace("module.", "")
+            # Force float16 to reduce size
+            clean_sd[new_key] = v.contiguous().to(torch.float16)
+
+    print("  Saving clean weights to Safetensors...")
+    save_file(clean_sd, OUTPUT_PATH)
+
+    new_size = os.path.getsize(OUTPUT_PATH) / (1024**3)
+    print(f"✅ Success! New size: {new_size:.2f} GB")
+
+if __name__ == "__main__":
+    convert()

model.safetensors

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