diff --git a/CMakeLists.txt b/CMakeLists.txt
index 79a6b1c44..417ef980b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -331,6 +331,11 @@ add_test(NAME bench_preflight COMMAND bash ${CMAKE_SOURCE_DIR}/tools/tests/test-
 # engine#1702: builds a per-layer MoE expert placement table from a moe_trace, and refuses a prefill-only trace because
 # placement is a decode lever (5.4-6.9 % on batched prefill against dedup's 90-94 %). --self-test drives it on synthetic
 # traces with a known answer; no model, no device.
+# engine#1934: reading ONE tensor out of a safetensors shard needs `data_offsets` measured from 8 + header_len, not
+# from byte 8. Getting that wrong returns a *different* tensor of the same size that still looks finite, and it cost
+# two false conclusions about a pack in one night. `--self-test` drives the rule on a synthetic shard (no network):
+# the same offset from the data section returns the tensor, from byte 8 it does not.
+add_python_test(hf_tensor_self ${CMAKE_SOURCE_DIR}/tools/hf_tensor.py --self-test)
 add_python_test(moe_placement ${CMAKE_SOURCE_DIR}/tools/moe_placement.py --self-test)
 # engine #1279: the version a package is built with (scripts/pack-version.sh) -- a release tag as
 # it stands, a candidate mapped into the ISO week of its own date with a suffix that sorts below that
diff --git a/tools/hf_tensor.py b/tools/hf_tensor.py
new file mode 100755
index 000000000..57f9b4b59
--- /dev/null
+++ b/tools/hf_tensor.py
@@ -0,0 +1,213 @@
+#!/usr/bin/env python3
+# Copyright 2026 bong-water-water-bong
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""hf_tensor.py -- read ONE tensor out of a safetensors shard, over HTTP range requests.
+
+Written because I got a safetensors offset wrong four times in one night and each time concluded something
+false about a pack. The rule that bit me, and the only reason this file exists:
+
+    a safetensors `data_offsets` entry is relative to the START OF THE DATA SECTION, i.e. `8 + header_len`
+    bytes into the file -- not to byte 8.
+
+Fetch `bytes=8+off` and you get a slice of whatever tensor is 11,656 B (this shard's header) away from the one
+you asked for, which still *looks* like a tensor: it has a plausible size, it is finite, and a correlation
+against it is a correlation against noise. Two of those cost me a retraction on a merged PR.
+
+So this tool does the arithmetic once, prints the tensor's own statistics so a wrong read is visible rather
+than plausible (a norm gain is ~1.0, not +-0.03), and can write the raw bytes for a comparison script.
+
+usage:
+  tools/hf_tensor.py --repo tencent/Hunyuan-7B-Instruct \
+      --tensor model.layers.0.self_attn.query_layernorm.weight [--out q.bin] [--json]
+  tools/hf_tensor.py --self-test
+
+Exit: 0 ok, 1 the tensor or shard was not found, 2 cannot tell (no network / not a safetensors repo).
+"""
+
+import argparse
+import json
+import pathlib
+import struct
+import sys
+import urllib.request
+
+UA = {"User-Agent": "1bit-aba/hf_tensor"}  # HF rejects the default python urllib agent on some endpoints
+DTYPE_TABLE = {
+    "F64": ("<d", 8), "F32": ("<f", 4), "F16": ("<e", 2), "BF16": (None, 2),
+    "I64": ("<q", 8), "I32": ("<i", 4), "I16": ("<h", 2), "I8": ("<b", 1),
+    "U8": ("<B", 1), "BOOL": ("<?", 1),
+}
+
+
+def http_range(url, start, end_inclusive):
+    """Inclusive byte range, as safetensors' data_offsets are written [start, end)."""
+    req = urllib.request.Request(url, headers={**UA, "Range": "bytes=%d-%d" % (start, end_inclusive)})
+    with urllib.request.urlopen(req, timeout=120) as r:
+        return r.read()
+
+
+def api_json(url):
+    req = urllib.request.Request(url, headers=UA)
+    with urllib.request.urlopen(req, timeout=60) as r:
+        return json.load(r)
+
+
+def data_section_start(first8):
+    """The byte the offsets are measured from: 8 + header_len. The whole point of this file."""
+    (hlen,) = struct.unpack("<Q", first8)
+    return 8 + hlen
+
+
+def values(raw, dtype):
+    """Decode a tensor's bytes to floats, for the statistics only (the raw bytes are what a caller compares)."""
+    if dtype == "BF16":
+        out = []
+        for i in range(0, len(raw) - 1, 2):
+            (u,) = struct.unpack("<H", raw[i:i + 2])
+            (v,) = struct.unpack("<f", struct.pack("<I", u << 16))
+            out.append(v)
+        return out
+    fmt, size = DTYPE_TABLE.get(dtype, (None, None))
+    if fmt is None:
+        return None
+    n = len(raw) // size
+    return list(struct.unpack("<%d%s" % (n, fmt[1]), raw[: n * size]))
+
+
+def read_tensor(repo, tensor, revision="main", base="https://huggingface.co"):
+    """(dtype, shape, raw_bytes, shard) for one tensor, reading only its header and its own bytes."""
+    idx = api_json("%s/%s/resolve/%s/model.safetensors.index.json" % (base, repo, revision))
+    shard = idx["weight_map"].get(tensor)
+    if shard is None:
+        raise KeyError("%s is not in %s's index (%d tensors)" % (tensor, repo, len(idx["weight_map"])))
+    url = "%s/%s/resolve/%s/%s" % (base, repo, revision, shard)
+    first8 = http_range(url, 0, 7)
+    if len(first8) != 8:
+        raise ValueError("%s: a range request returned %d bytes, not 8" % (url, len(first8)))
+    dstart = data_section_start(first8)
+    header = json.loads(http_range(url, 8, dstart - 1).decode("utf-8", "replace").rstrip("\0 "))
+    meta = header.get(tensor)
+    if meta is None:
+        raise KeyError("%s is not in %s's header" % (tensor, shard))
+    lo, hi = meta["data_offsets"]
+    if hi < lo:
+        raise ValueError("%s: data_offsets %r are inverted" % (tensor, meta["data_offsets"]))
+    # the rule this file exists for
+    raw = http_range(url, dstart + lo, dstart + hi - 1)
+    return meta.get("dtype", ""), meta.get("shape", []), raw, shard
+
+
+def describe(dtype, shape, raw):
+    v = values(raw, dtype)
+    if not v:
+        return {"dtype": dtype, "shape": shape, "bytes": len(raw), "decoded": False}
+    fin = [x for x in v if x == x and abs(x) != float("inf")]
+    return {
+        "dtype": dtype,
+        "shape": shape,
+        "bytes": len(raw),
+        "decoded": True,
+        "n": len(v),
+        "mean": sum(fin) / len(fin) if fin else None,
+        "min": min(fin) if fin else None,
+        "max": max(fin) if fin else None,
+        "nonfinite": len(v) - len(fin),
+    }
+
+
+def self_test():
+    """The offset rule on a synthetic shard, and the statistics on a synthetic norm gain."""
+    import tempfile
+
+    failures = 0
+
+    def check(ok, what):
+        nonlocal failures
+        print(("ok   " if ok else "FAIL ") + what)
+        if not ok:
+            failures += 1
+
+    d = pathlib.Path(tempfile.mkdtemp())
+    # two tensors, the FIRST one deliberately long so the second's offset is past it (the 11,656-byte trap)
+    first = bytes(range(256)) * 45           # 11,520 B
+    second = struct.pack("<128H", *[struct.unpack("<H", struct.pack("<e", 1.5))[0]] * 128)  # F16 [128] of 1.5
+    hdr = json.dumps({
+        "a.first": {"dtype": "U8", "shape": [len(first)], "data_offsets": [0, len(first)]},
+        "b.second": {"dtype": "F16", "shape": [128], "data_offsets": [len(first), len(first) + len(second)]},
+    }).encode()
+    shard = d / "model.safetensors"
+    shard.write_bytes(struct.pack("<Q", len(hdr)) + hdr + first + second)
+
+    blob = shard.read_bytes()
+    start = data_section_start(blob[:8])
+    check(start == 8 + len(hdr), "the data section starts at 8 + header_len, not at 8")
+    (hlen,) = struct.unpack("<Q", blob[:8])
+    header = json.loads(blob[8:start].decode())
+    lo, hi = header["b.second"]["data_offsets"]
+    right = blob[start + lo:start + hi]
+    wrong = blob[8 + lo:8 + hi]
+    check(right == second, "the offset applied from the data section returns the tensor")
+    check(wrong != second, "... and the same offset applied from byte 8 does NOT (the mistake this file prevents)")
+    stats = describe("F16", [128], right)
+    check(abs(stats["mean"] - 1.5) < 1e-6 and stats["min"] == stats["max"] == 1.5,
+          "statistics make a norm gain visible (mean 1.5, not a plausible-looking slice of another tensor)")
+    if failures:
+        print("self-test: %d failure(s)" % failures)
+    else:
+        print("self-test: ok")
+    return 1 if failures else 0
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    ap.add_argument("--self-test", action="store_true")
+    ap.add_argument("--repo")
+    ap.add_argument("--tensor")
+    ap.add_argument("--revision", default="main")
+    ap.add_argument("--out", help="write the raw tensor bytes here (what a comparison script consumes)")
+    ap.add_argument("--json", action="store_true")
+    a = ap.parse_args()
+    if a.self_test:
+        return self_test()
+    if not a.repo or not a.tensor:
+        ap.print_help()
+        return 2
+    try:
+        dtype, shape, raw, shard = read_tensor(a.repo, a.tensor, a.revision)
+    except KeyError as e:
+        print("hf_tensor: %s" % e, file=sys.stderr)
+        return 1
+    except (ValueError, OSError) as e:
+        print("hf_tensor: cannot tell: %s" % e, file=sys.stderr)
+        return 2
+    stats = describe(dtype, shape, raw)
+    stats["tensor"] = a.tensor
+    stats["shard"] = shard
+    if a.out:
+        pathlib.Path(a.out).write_bytes(raw)
+        stats["wrote"] = a.out
+    if a.json:
+        print(json.dumps(stats))
+    else:
+        print("%s  %s %s  %d B  (%s)" % (a.tensor, dtype, shape, len(raw), shard))
+        if stats["decoded"]:
+            print("  mean %+.6f  min %+.6f  max %+.6f  nonfinite %d" %
+                  (stats["mean"], stats["min"], stats["max"], stats["nonfinite"]))
+            print("  a norm gain is ~1.0 and a weight matrix is ~0: check these before trusting a correlation")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())