diff --git a/c/tools/convert_fp8_to_int4.py b/c/tools/convert_fp8_to_int4.py
index 888e324..eac51f9 100644
--- a/c/tools/convert_fp8_to_int4.py
+++ b/c/tools/convert_fp8_to_int4.py
@@ -286,6 +286,17 @@ def main():
         # testa MTP a int4 = acceptance ~0-4% (misurato, issue #8): il draft sbaglia sempre
         # e la speculazione non parte mai. A int8: 39-59%, 2.2-2.8 token/forward.
         a.ebits = 8 if (a.mtp or a.indexer) else 4
+    if a.mtp and a.ebits < 8 and a.group_size <= 0:
+        # Non solo lossy: eh_proj ha ~20-30x di asimmetria di scala fra le due meta' di
+        # colonna, quindi l'int4 per-riga (UNA scala per riga) arrotonda a ZERO l'intera
+        # meta' embedding -> il draft non vede il token -> acceptance ~0% (issue #8).
+        # EN: not merely lossy: eh_proj has ~20-30x column-scale asymmetry, so per-row
+        # EN: int4 rounds its ENTIRE embedding half to exact zeros -> the draft cannot
+        # EN: see the input token -> ~0% acceptance (issue #8). A container converted
+        # EN: this way is repairable in place with tools/repair_mtp_int8.py.
+        print(f"WARNING: --mtp with --ebits {a.ebits} and per-row scales ZEROES eh_proj's "
+              "embedding half -> MTP acceptance ~0% (issue #8). Use the default --ebits 8, "
+              "or add --group-size 128 for group-scaled int4.")
     if a.xbits is None: a.xbits = a.ebits
 
     # Build per-type bits map. If a type-specific arg is set, use it; otherwise the
diff --git a/c/tools/repair_mtp_int8.py b/c/tools/repair_mtp_int8.py
new file mode 100644
index 0000000..695e99f
--- /dev/null
+++ b/c/tools/repair_mtp_int8.py
@@ -0,0 +1,229 @@
+"""Repair an existing colibri int4 container whose MTP head was quantized at int4.
+
+WHY THIS EXISTS
+  `model.layers.<N>.eh_proj.weight` [D, 2D] multiplies the MTP concat
+  [embedding_norm ; hidden_norm], and its two column halves differ in scale by
+  ~20-30x per row (embedding-half absmax ~0.05, hidden-half ~1.5 on GLM-5.2).
+  Per-row int4 uses ONE scale (= absmax/7) per row, so every embedding-half
+  weight lands below half a quantization step and np.rint rounds the ENTIRE
+  embedding half to exact zeros (packed bytes 0x88). The MTP head then drafts
+  garbage: acceptance ~0% (issue #8 measured 0-4% at int4; 39-59% at int8 —
+  which is why `convert_fp8_to_int4.py --mtp` defaults to --ebits 8).
+
+  A container converted (or downloaded) with an int4 MTP head does not need a
+  full re-conversion: this script re-downloads ONLY the affected dense tensors
+  (~355 MB of HTTP range reads against the FP8 source repo), requantizes them
+  at int8 with the converter's exact math, and patches the local shards in
+  place. Originals are kept beside as *.bak-int4.
+
+WHAT IT TOUCHES
+  The MTP layer's dense tensors only (eh_proj, q_a/q_b/kv_a/kv_b/o_proj,
+  shared_experts.*): the ones that stream into RAM once and stay resident.
+  Routed experts (model.layers.<N>.mlp.experts.*) are NOT touched — they are
+  statistically like the main layers' experts and int4 is acceptable there.
+  The engine auto-detects int8 vs int4 by blob size (qt_from_disk), so no
+  engine or config change is needed. Cost: ~+133 MB on disk / resident RAM.
+
+USAGE
+  python3 tools/repair_mtp_int8.py --snap /path/to/glm52_i4              # repair
+  python3 tools/repair_mtp_int8.py --snap /path/to/glm52_i4 --dry-run    # inspect only
+
+  --source-repo defaults to zai-org/GLM-5.2-FP8 (the checkpoint the public
+  int4 containers were converted from). Requires numpy and network access;
+  no torch, no HF token (public repo, anonymous range reads).
+"""
+import argparse, glob, json, os, ssl, struct, sys, urllib.request
+import numpy as np
+
+# macOS python.org builds ship no CA bundle: use certifi when available (Linux
+# system Pythons generally have working system certs and skip this).
+try:
+    import certifi
+    _SSL_CTX = ssl.create_default_context(cafile=certifi.where())
+except ImportError:
+    _SSL_CTX = ssl.create_default_context()
+
+
+# ---------- HTTP range reads against the source repo ----------
+def http_range(url, start, length, tries=5):
+    req = urllib.request.Request(url, headers={"User-Agent": "colibri-mtp-repair",
+                                               "Range": f"bytes={start}-{start+length-1}"})
+    for attempt in range(tries):
+        try:
+            with urllib.request.urlopen(req, timeout=30, context=_SSL_CTX) as r:
+                data = r.read()
+            if len(data) == length:
+                return data
+        except KeyboardInterrupt:
+            raise
+        except Exception as ex:
+            if attempt == tries - 1:
+                raise RuntimeError(f"range read failed for {url}: {ex}")
+    raise RuntimeError(f"short range read for {url}")
+
+
+class SourceRepo:
+    def __init__(self, repo, revision="main"):
+        self.base = f"https://huggingface.co/{repo}/resolve/{revision}/"
+        with urllib.request.urlopen(self.base + "model.safetensors.index.json", timeout=30, context=_SSL_CTX) as r:
+            self.wmap = json.loads(r.read())["weight_map"]
+        self._hdr = {}
+
+    def _shard_header(self, shard):
+        if shard not in self._hdr:
+            n = struct.unpack("<Q", http_range(self.base + shard, 0, 8))[0]
+            self._hdr[shard] = (json.loads(http_range(self.base + shard, 8, n)), 8 + n)
+        return self._hdr[shard]
+
+    def meta(self, name):
+        shard = self.wmap.get(name)
+        if not shard:
+            return None
+        hdr, _ = self._shard_header(shard)
+        return hdr[name]
+
+    def fetch_f32(self, name):
+        """Download one tensor and dequantize to f32 [O, I] (BF16 or FP8+block scales)."""
+        shard = self.wmap[name]
+        hdr, base = self._shard_header(shard)
+        m = hdr[name]
+        o0, o1 = m["data_offsets"]
+        raw = http_range(self.base + shard, base + o0, o1 - o0)
+        if m["dtype"] == "BF16":
+            u = np.frombuffer(raw, dtype=np.uint16).astype(np.uint32) << 16
+            return u.view(np.float32).reshape(m["shape"]).astype(np.float32)
+        if m["dtype"] == "F8_E4M3":
+            b = np.frombuffer(raw, dtype=np.uint8).astype(np.uint16)
+            sign = np.where(b & 0x80, -1.0, 1.0)
+            e = (b >> 3) & 0xF
+            mant = (b & 7).astype(np.float64)
+            v = (sign * np.where(e > 0, (1 + mant / 8) * np.exp2(e.astype(np.float64) - 7),
+                                 mant / 8 * np.exp2(-6.0))).reshape(m["shape"])
+            sn = name + "_scale_inv"
+            sshard = self.wmap[sn]
+            shdr, sbase = self._shard_header(sshard)
+            sm = shdr[sn]
+            so0, so1 = sm["data_offsets"]
+            sc = np.frombuffer(http_range(self.base + sshard, sbase + so0, so1 - so0),
+                               dtype=np.float32).reshape(sm["shape"])
+            O, I = m["shape"]
+            scf = np.repeat(np.repeat(sc, 128, axis=0)[:O], 128, axis=1)[:, :I]
+            return (v * scf).astype(np.float32)
+        raise ValueError(f"{name}: unsupported source dtype {m['dtype']}")
+
+
+# ---------- quantization: identical to convert_fp8_to_int4.quant_int8 ----------
+def quant_int8(w):
+    amax = np.abs(w).max(axis=1, keepdims=True)
+    s = np.maximum(amax / 127, 1e-8)
+    q = np.clip(np.rint(w / s), -128, 127).astype(np.int8)
+    return q.reshape(-1).view(np.uint8).copy(), s[:, 0].astype(np.float32)
+
+
+# ---------- local safetensors IO (no deps; preserves byte-identity of untouched tensors) ----------
+def read_shard(path):
+    with open(path, "rb") as fh:
+        n = struct.unpack("<Q", fh.read(8))[0]
+        hdr = json.loads(fh.read(n))
+        base = 8 + n
+        order = sorted(((k, v) for k, v in hdr.items() if k != "__metadata__"),
+                       key=lambda kv: kv[1]["data_offsets"][0])
+        out = {}
+        for k, v in order:
+            fh.seek(base + v["data_offsets"][0])
+            out[k] = (v["dtype"], v["shape"], fh.read(v["data_offsets"][1] - v["data_offsets"][0]))
+    return out, hdr.get("__metadata__")
+
+
+def write_shard(path, tensors, meta):
+    hdr = {}
+    off = 0
+    for k, (dt, shape, raw) in tensors.items():
+        hdr[k] = {"dtype": dt, "shape": shape, "data_offsets": [off, off + len(raw)]}
+        off += len(raw)
+    if meta:
+        hdr["__metadata__"] = meta
+    hj = json.dumps(hdr).encode()
+    hj += b" " * ((8 - len(hj) % 8) % 8)
+    with open(path, "wb") as fh:
+        fh.write(struct.pack("<Q", len(hj)))
+        fh.write(hj)
+        for _, (_, _, raw) in tensors.items():
+            fh.write(raw)
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--snap", required=True, help="local colibri int4 container directory")
+    ap.add_argument("--source-repo", default="zai-org/GLM-5.2-FP8")
+    ap.add_argument("--revision", default="main")
+    ap.add_argument("--dry-run", action="store_true", help="report what would change, touch nothing")
+    a = ap.parse_args()
+
+    cfg = json.load(open(os.path.join(a.snap, "config.json")))
+    L = cfg["num_hidden_layers"]
+    pref = f"model.layers.{L}."
+    src = SourceRepo(a.source_repo, a.revision)
+
+    # find the MTP layer's dense int4 tensors across the local shards
+    plan = {}   # shard path -> [tensor names to repair]
+    already_ok, skipped_experts = [], 0
+    for f in sorted(glob.glob(os.path.join(a.snap, "*.safetensors"))):
+        with open(f, "rb") as fh:
+            n = struct.unpack("<Q", fh.read(8))[0]
+            hdr = json.loads(fh.read(n))
+        for name, v in hdr.items():
+            if not name.startswith(pref) or name.endswith(".qs") or v.get("dtype") != "U8":
+                continue
+            if ".mlp.experts." in name:
+                skipped_experts += 1
+                continue
+            m = src.meta(name)
+            if m is None:
+                print(f"  ?? {name}: not in source repo, skipping")
+                continue
+            O, I = m["shape"]
+            nb = v["data_offsets"][1] - v["data_offsets"][0]
+            if nb == O * I:
+                already_ok.append(name)
+            elif nb == O * ((I + 1) // 2):
+                plan.setdefault(f, []).append((name, O, I))
+            else:
+                print(f"  ?? {name}: unexpected blob size {nb} for shape {O}x{I}, skipping")
+
+    n_fix = sum(len(v) for v in plan.values())
+    print(f"MTP layer {L}: {n_fix} dense tensor(s) at per-row int4 to repair, "
+          f"{len(already_ok)} already int8, {skipped_experts} routed-expert tensors left as-is")
+    if not n_fix:
+        print("nothing to do."); return
+    if a.dry_run:
+        for f, names in plan.items():
+            for nm, O, I in names:
+                print(f"  would repair {nm} [{O},{I}] in {os.path.basename(f)}")
+        return
+
+    for f, names in plan.items():
+        print(f"patching {os.path.basename(f)} ({len(names)} tensors)")
+        tensors, meta = read_shard(f)
+        for nm, O, I in names:
+            print(f"  {nm}: fetching source + requantizing at int8...", flush=True)
+            w = src.fetch_f32(nm)
+            assert w.shape == (O, I), f"{nm}: source shape {w.shape} != container {O}x{I}"
+            q, s = quant_int8(w)
+            tensors[nm] = ("U8", [len(q)], q.tobytes())
+            tensors[nm + ".qs"] = ("F32", [O], s.tobytes())
+            # verify first row against the source
+            loc = q[:I].view(np.int8).astype(np.float64) * s[0]
+            ref = w[0].astype(np.float64)
+            cos = float(ref @ loc / (np.linalg.norm(ref) * np.linalg.norm(loc) + 1e-30))
+            print(f"    row-0 cosine vs source: {cos:.5f}")
+        bak = f + ".bak-int4"
+        write_shard(f + ".new", tensors, meta)
+        os.replace(f, bak)
+        os.replace(f + ".new", f)
+        print(f"  saved; original kept as {os.path.basename(bak)}")
+    print("done. Re-run with --dry-run to confirm (should report 'already int8').")
+
+
+if __name__ == "__main__":
+    main()