Stage 1 on HF Jobs: Unsloth job script, PEFT to MLX converter, base valid loss 0.849, Qwen base model docs

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>

train/peft_to_mlx.py

@@ -0,0 +1,44 @@
+"""Convert a PEFT LoRA adapter (Unsloth / HF) to the mlx-lm adapter format.
+
+  train/.venv/bin/python train/peft_to_mlx.py <peft_adapter_dir> <mlx_adapter_dir>
+
+PEFT key  : base_model.model.model.language_model.layers.N.<mod>.lora_A.weight   shape (r, in)
+            base_model.model.model.language_model.layers.N.<mod>.lora_B.weight   shape (out, r)
+mlx key   : language_model.model.layers.N.<mod>.lora_a                           shape (in, r)
+            language_model.model.layers.N.<mod>.lora_b                           shape (r, out)
+Scale     : PEFT scaling = alpha / r; mlx `scale` is used directly (y + scale * x @ A @ B), so scale = alpha / r.
+mlx loads only the modules listed in `lora_parameters.keys` (relative to the layer); they are taken from the file.
+"""
+import json, re, sys
+from pathlib import Path
+
+import mlx.core as mx
+
+src, dst = Path(sys.argv[1]), Path(sys.argv[2])
+cfg = json.load(open(src / "adapter_config.json"))
+r, alpha = cfg["r"], cfg["lora_alpha"]
+if cfg.get("use_rslora") or cfg.get("use_dora"):
+    sys.exit("rsLoRA / DoRA: scale is not alpha / r, not supported")
+
+w = mx.load(str(src / "adapter_model.safetensors"))
+pat = re.compile(r"^base_model\.model\.model\.language_model\.layers\.(\d+)\.(.+)\.lora_([AB])\.weight$")
+out, keys, layers = {}, set(), set()
+for k, v in w.items():
+    m = pat.match(k)
+    if not m:
+        sys.exit(f"unexpected key: {k}")
+    n, mod, ab = m.groups()
+    layers.add(int(n))
+    keys.add(mod)
+    out[f"language_model.model.layers.{n}.{mod}.lora_{ab.lower()}"] = mx.transpose(v).astype(mx.float32)
+    if ab == "A":
+        assert v.shape[0] == r, (k, v.shape)
+
+dst.mkdir(parents=True, exist_ok=True)
+mx.save_safetensors(str(dst / "adapters.safetensors"), out)
+json.dump({
+    "fine_tune_type": "lora",
+    "num_layers": max(layers) + 1,
+    "lora_parameters": {"rank": r, "scale": alpha / r, "dropout": 0.0, "keys": sorted(keys)},
+}, open(dst / "adapter_config.json", "w"), indent=1)
+print(f"{len(out)} tensors, {len(layers)} layers, rank {r}, alpha {alpha} -> scale {alpha / r}, modules {sorted(keys)}")