diff --git a/utils/atlas_ternary_packer.py b/utils/atlas_ternary_packer.py
new file mode 100644
index 000000000..afb6b4420
--- /dev/null
+++ b/utils/atlas_ternary_packer.py
@@ -0,0 +1,87 @@
+"""
+ATLAS Ternary Packer - For BitNet GGUF Converter
+=========================================
+Add to utils/convert-hf-to-gguf-bitnet.py
+
+This module provides 4x compression for ternary BitNet weights.
+Insert at line ~159 in write_tensors() method.
+"""
+
+import numpy as np
+
+
+def detect_ternary(values: np.ndarray, threshold: float = 0.1) -> bool:
+    """Detect if tensor contains only ternary values {-1, 0, +1}"""
+    unique = np.unique(np.round(values, 1))
+    ternary_vals = {-1.0, 0.0, 1.0}
+    return all(abs(v) < threshold or v in ternary_vals for v in unique)
+
+
+def quantize_ternary(values: np.ndarray) -> np.ndarray:
+    """Quantize floating point to ternary {-1, 0, +1}"""
+    return np.where(values > 0.1, 1, np.where(values < -0.1, -1, 0))
+
+
+def pack_ternary(ternary_arr: np.ndarray) -> np.ndarray:
+    """Pack ternary values into 2-bit representation (4x compression)
+    
+    Encoding:
+      -1 → 0b00
+       0 → 0b01
+      +1 → 0b10
+    """
+    TERNAMAP = {-1: 0b00, 0: 0b01, 1: 0b10}
+    
+    if len(ternary_arr) % 4 != 0:
+        pad = 4 - (len(ternary_arr) % 4)
+        ternary_arr = np.pad(ternary_arr, (0, pad), constant_values=0)
+    
+    packed = []
+    for i in range(0, len(ternary_arr), 4):
+        bits = 0
+        for j, val in enumerate(ternary_arr[i:i+4]):
+            bits |= (TERNAMAP.get(int(val), 0b11) << (j * 2))
+        packed.append(bits)
+    
+    return np.array(packed, dtype=np.uint8)
+
+
+def unpack_ternary(packed: np.ndarray, length: int) -> np.ndarray:
+    """Unpack 2-bit representation back to ternary"""
+    INVERSE = {0b00: -1, 0b01: 0, 0b10: 1}
+    result = []
+    
+    for bits in packed:
+        for j in range(4):
+            if len(result) < length:
+                code = (bits >> (j * 2)) & 0b11
+                result.append(INVERSE.get(code, 0))
+    
+    return np.array(result[:length], dtype=np.int8)
+
+
+# Integration point: Add to convert-hf-to-gguf-bitnet.py
+# In write_tensors() method, around line 159:
+#
+# def write_tensors(self):
+#     # [INSERT ATLAS PACKER HERE]
+#     if detect_ternary(data):
+#         packed = pack_ternary(quantize_ternary(data))
+#         self.gguf_writer.add_tensor(new_name, packed, tensor_type=gguf.GGMLQuantizationType.TL1)
+#     else:
+#         self.gguf_writer.add_tensor(new_name, data)
+
+
+# Test
+if __name__ == "__main__":
+    # Test with 8 values = 2 bytes packed (2-bit per value)
+    test = np.array([-1.0, 0.0, 1.0, 1.0, -1.0, 0.0, 1.0, -1.0], dtype=np.float32)
+    print(f"Original: {test.nbytes} bytes (8 x float32)")
+    
+    ternary = quantize_ternary(test)
+    packed = pack_ternary(ternary)
+    print(f"Packed:   {packed.nbytes} bytes (8 x 2-bit)")
+    print(f"Compression: {test.nbytes / packed.nbytes:.1f}x")
+    
+    restored = unpack_ternary(packed, len(test))
+    print(f"Round-trip OK: {np.array_equal(ternary, restored)}")
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diff --git a/utils/atlas_tq1_0_validation.py b/utils/atlas_tq1_0_validation.py
new file mode 100644
index 000000000..c81ca1286
--- /dev/null
+++ b/utils/atlas_tq1_0_validation.py
@@ -0,0 +1,81 @@
+import numpy as np
+import os
+
+# --- TQ1_0 Ternary Packing Logic ---
+
+def quantize_ternary(values):
+    """Quantizes float values to ternary scale (-1, 0, 1)."""
+    return np.where(values > 0.1, 1, np.where(values < -0.1, -1, 0))
+
+def pack_ternary(ternary_arr):
+    """Packs 4 ternary values into a single uint8 (2 bits per value)."""
+    TERNAMAP = {-1: 0b00, 0: 0b01, 1: 0b10}
+    original_len = len(ternary_arr)
+    
+    # Padding to ensure length is a multiple of 4
+    if original_len % 4 != 0:
+        pad = 4 - (original_len % 4)
+        ternary_arr = np.pad(ternary_arr, (0, pad), constant_values=0)
+    
+    packed = []
+    for i in range(0, len(ternary_arr), 4):
+        bits = 0
+        for j, val in enumerate(ternary_arr[i:i+4]):
+            # Default to 0b01 (ternary 0) if value is unexpected
+            bits |= (TERNAMAP.get(int(val), 0b01) << (j * 2))
+        packed.append(bits)
+    return np.array(packed, dtype=np.uint8)
+
+def unpack_ternary(packed, length):
+    """Unpacks uint8 back to ternary values (-1, 0, 1)."""
+    INVERSE = {0b00: -1, 0b01: 0, 0b10: 1, 0b11: 0}
+    result = []
+    for bits in packed:
+        for j in range(4):
+            if len(result) < length:
+                code = (bits >> (j * 2)) & 0b11
+                result.append(INVERSE.get(code, 0))
+    return np.array(result, dtype=np.int8)
+
+# --- Performance & Integrity Audit ---
+
+def run_audit(sample_size=1_000_000):
+    print(f"--- TQ1_0 VALIDATION AUDIT (Reference: Float16) ---")
+    print(f"Sample Size: {sample_size} weights")
+
+    # 1. Generate random weights in FP16
+    raw_data = np.random.uniform(-1, 1, sample_size).astype(np.float16)
+
+    # 2. Process: Quantize and Pack
+    ternary = quantize_ternary(raw_data)
+    packed_data = pack_ternary(ternary)
+
+    # 3. I/O Simulation: Write to disk
+    tmp_file = "temp_weights.bin"
+    packed_data.tofile(tmp_file)
+    
+    # 4. Read back and Unpack
+    disk_data = np.fromfile(tmp_file, dtype=np.uint8)
+    restored = unpack_ternary(disk_data, sample_size)
+
+    # 5. Calculate Metrics
+    fp16_size_kb = raw_data.nbytes / 1024
+    packed_size_kb = os.path.getsize(tmp_file) / 1024
+    reduction = (1 - (packed_size_kb / fp16_size_kb)) * 100
+
+    print(f"Original Size (FP16): {fp16_size_kb:.2f} KB")
+    print(f"Packed Size (TQ1_0):   {packed_size_kb:.2f} KB")
+    print(f"Memory Reduction:      {reduction:.2f}%")
+
+    # 6. Integrity Check
+    if np.array_equal(ternary, restored):
+        print("\n✅ STATUS: BIT-EXACT RECONSTRUCTION SUCCESSFUL.")
+    else:
+        print("\n❌ STATUS: INTEGRITY CHECK FAILED.")
+
+    # Cleanup
+    if os.path.exists(tmp_file):
+        os.remove(tmp_file)
+
+if __name__ == "__main__":
+    run_audit()
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