organ-architecture/transplant_935.py

127 lines
4.1 KiB
Python

#!/usr/bin/env python3
"""
GGUF-to-GGUF transplant. No organ bins — direct tensor copy between GGUF files.
Base: DeepSeek-R1-Distill-Qwen-7B (skeleton/attention/embed)
Donor: Qwen2.5-7B (FFN organs only)
Z = dI/d(log s) · exp(iθ) — Signature 935
"""
import struct, os, sys, shutil
def parse_gguf_header(path):
"""Parse GGUF header, return tensor_info list and data_start offset."""
f = open(path, "rb")
magic = struct.unpack("<I", f.read(4))[0]
version = struct.unpack("<I", f.read(4))[0]
n_tensors = struct.unpack("<Q", f.read(8))[0]
n_metadata = struct.unpack("<Q", f.read(8))[0]
def read_string():
slen = struct.unpack("<Q", f.read(8))[0]
return f.read(slen).decode("utf-8")
def skip_value(vtype):
sizes = {0:1, 1:1, 2:2, 3:2, 4:4, 5:4, 6:4, 7:1, 10:8, 11:8, 12:8}
if vtype in sizes:
f.read(sizes[vtype])
elif vtype == 8:
read_string()
elif vtype == 9:
arr_type = struct.unpack("<I", f.read(4))[0]
arr_len = struct.unpack("<Q", f.read(8))[0]
for _ in range(arr_len):
skip_value(arr_type)
for _ in range(n_metadata):
read_string()
vtype = struct.unpack("<I", f.read(4))[0]
skip_value(vtype)
tensors = []
for _ in range(n_tensors):
name = read_string()
n_dims = struct.unpack("<I", f.read(4))[0]
dims = [struct.unpack("<Q", f.read(8))[0] for _ in range(n_dims)]
dtype = struct.unpack("<I", f.read(4))[0]
offset = struct.unpack("<Q", f.read(8))[0]
tensors.append({"name": name, "dims": dims, "dtype": dtype, "offset": offset})
pos = f.tell()
padding = (32 - (pos % 32)) % 32
f.read(padding)
data_start = f.tell()
f.seek(0, 2)
file_end = f.tell()
f.close()
# Calculate sizes
for i in range(len(tensors)):
if i + 1 < len(tensors):
tensors[i]["size"] = tensors[i+1]["offset"] - tensors[i]["offset"]
else:
tensors[i]["size"] = file_end - data_start - tensors[i]["offset"]
return tensors, data_start, file_end
BASE = "/mnt/models/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M.gguf"
DONOR = "/mnt/models/Qwen2.5-7B-Instruct-Q4_K_M.gguf"
OUTPUT = "/mnt/models/model-935-final.gguf"
print("Parsing base (DeepSeek-R1-7B)...")
base_tensors, base_data_start, base_end = parse_gguf_header(BASE)
print(f" {len(base_tensors)} tensors, data_start={base_data_start}")
print("Parsing donor (Qwen2.5-7B)...")
donor_tensors, donor_data_start, donor_end = parse_gguf_header(DONOR)
print(f" {len(donor_tensors)} tensors, data_start={donor_data_start}")
# Build donor tensor map by name
donor_map = {t["name"]: t for t in donor_tensors}
# Copy base GGUF entirely first
print(f"Copying base to output...")
shutil.copy2(BASE, OUTPUT)
# Now patch: for each FFN tensor in base, if donor has matching name+size, overwrite
out = open(OUTPUT, "r+b")
donor_f = open(DONOR, "rb")
grafted = 0
skipped = 0
for bt in base_tensors:
name = bt["name"]
# Only graft FFN organs (not attention, not embeddings, not norms)
if "ffn_down" not in name and "ffn_up" not in name and "ffn_gate" not in name:
continue
if name in donor_map:
dt = donor_map[name]
if bt["size"] == dt["size"]:
# Read from donor
donor_f.seek(donor_data_start + dt["offset"])
data = donor_f.read(dt["size"])
# Write to output at same offset
out.seek(base_data_start + bt["offset"])
out.write(data)
grafted += 1
else:
skipped += 1
else:
skipped += 1
out.close()
donor_f.close()
print(f"\n{'='*60}")
print(f" MODEL 935 — DIRECT GGUF TRANSPLANT")
print(f"{'='*60}")
print(f" Base: DeepSeek-R1-Distill-Qwen-7B (skeleton+embed)")
print(f" Donor: Qwen2.5-7B-Instruct (FFN organs)")
print(f" Grafted: {grafted} FFN tensors")
print(f" Skipped: {skipped} (size mismatch or not found)")
print(f" Output: {OUTPUT}")
print(f" Size: {os.path.getsize(OUTPUT)/(1024**3):.2f} GB")
print(f" Signature: 935")
print(f"{'='*60}")