| """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) |
|
|
| |
| 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(): |
| 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) |
| 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())) |
|
|
