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deaf348 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | """Build the FluidInference/vela-3.0-coreml release folder (release/):
Vela3Decision.mlpackage multifunction fp16 encoder (decision backbone), functions L128 / L256 / L512 / L1024
Vela3SpanSets.mlpackage multifunction fp16 encoder (backbone + span_sets adapter, LoRA merged), functions L512 / L1024
heads_decision.safetensors, heads_span_sets.safetensors fp32 heads (run on the host)
tokenizer.json (base tokenizer, the 8 marker tokens removed: markers are inserted by id), config.json, vela3_config.json
upstream/ the release's own Python (Apache-2.0, unmodified) that renders inputs, decodes spans and picks thresholds"""
import json, shutil, sys
from pathlib import Path
import coremltools as ct
import torch
from safetensors.torch import load_file, save_file
OUT = Path("release"); OUT.mkdir(exist_ok=True)
for name, src, Ls in (("Vela3Decision", "vela3_decision", (128, 256, 512, 1024)), ("Vela3SpanSets", "vela3_span_sets", (512, 1024))):
d = ct.utils.MultiFunctionDescriptor()
for L in Ls:
d.add_function(f"coreml/{src}_L{L}.mlpackage", src_function_name="main", target_function_name=f"L{L}")
d.default_function_name = f"L{Ls[0]}"
ct.utils.save_multifunction(d, str(OUT / f"{name}.mlpackage"))
print("packed", name)
# heads: decision (from model.safetensors) and span_sets (the adapter's own heads; everything outside the encoder)
sd = load_file("src/model.safetensors")
save_file({k: v.float().contiguous() for k, v in sd.items() if not k.startswith("encoder.")}, str(OUT / "heads_decision.safetensors"))
ad = load_file("src/span_sets_adapter.safetensors")
heads = {k: v.float().contiguous() for k, v in ad.items() if not k.startswith("encoder.")}
for k, v in sd.items(): # heads the adapter did not change come from the backbone file
if not k.startswith("encoder.") and k not in heads:
heads[k] = v.float().contiguous()
save_file(heads, str(OUT / "heads_span_sets.safetensors"))
print("heads", sorted(heads))
cfg = json.load(open("src/config.json")); mk = cfg["decision"]["marker_ids"]
tj = json.load(open("src/tokenizer.json", encoding="utf-8"))
json.dump(tj, open(OUT / "tokenizer.json", "w", encoding="utf-8"), ensure_ascii=False) # the release's own file (its loaders strip the markers)
for f in ("config.json", "vela3_config.json", "tokenizer_config.json", "LICENSE"):
shutil.copy(f"src/{f}", OUT / f)
up = OUT / "upstream"; up.mkdir(exist_ok=True)
for f in ("vela3_encoder.py", "vela3_schema.py", "ub_d3_common.py"):
shutil.copy(f"src/{f}", up / f)
print("done", sorted(p.name for p in OUT.iterdir()))
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