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adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "openai/gpt-oss-120b",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": "all-linear",
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b1eadb86093bba917a6b603e1786cd7b03c0175f1ee965955cc290983f346449
+size 5257620504

merge.py

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+"""Merge LoRA adapter into base model weights.
+
+Usage:
+    pip install torch transformers safetensors tqdm huggingface-hub
+    python merge.py --output ./merged_model
+
+Loads the MXFP4 base model, dequantizes to bf16, applies LoRA deltas, saves merged model.
+"""
+
+import argparse
+import json
+import shutil
+from pathlib import Path
+
+import torch
+from huggingface_hub import snapshot_download
+from safetensors.torch import load_file, save_file
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM
+
+BASE_MODEL = "openai/gpt-oss-120b"
+ADAPTER_REPO = "LightningRodLabs/Golf-Forecaster"
+
+
+def merge(output_dir: str):
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Download adapter
+    print("Downloading adapter...")
+    adapter_dir = Path(snapshot_download(ADAPTER_REPO))
+    adapter_config = json.loads((adapter_dir / "adapter_config.json").read_text())
+    scaling = adapter_config["lora_alpha"] / adapter_config["r"]
+    adapter_weights = load_file(str(adapter_dir / "adapter_model.safetensors"))
+    print(f"Adapter: {len(adapter_weights)} keys, scaling={scaling}")
+
+    # Load base model (dequantizes MXFP4 to bf16)
+    print("Loading base model...")
+    base_model = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL, torch_dtype=torch.bfloat16, device_map="cpu", trust_remote_code=True,
+    )
+    state_dict = base_model.state_dict()
+    del base_model
+
+    # Group LoRA A/B pairs
+    lora_pairs = {}
+    for key, tensor in adapter_weights.items():
+        clean = key.replace("base_model.model.", "", 1)
+        if ".lora_A.weight" in clean:
+            lora_pairs.setdefault(clean.replace(".lora_A.weight", ""), {})["A"] = tensor
+        elif ".lora_B.weight" in clean:
+            lora_pairs.setdefault(clean.replace(".lora_B.weight", ""), {})["B"] = tensor
+
+    # Map adapter keys to base model keys + merge operation
+    # Adapter uses Tinker naming; HF transformers uses different names:
+    #   attn -> self_attn, w1(gate)/w3(up) -> gate_up_proj (interleaved), w2 -> down_proj
+    base_key_ops = {}
+    for adapter_path in lora_pairs:
+        if "unembed_tokens" in adapter_path:
+            base_key_ops.setdefault("lm_head.weight", []).append(("add", adapter_path))
+        elif ".attn." in adapter_path:
+            base_key = adapter_path.replace(".attn.", ".self_attn.") + ".weight"
+            base_key_ops.setdefault(base_key, []).append(("add", adapter_path))
+        elif ".mlp.experts.w1" in adapter_path:
+            prefix = adapter_path.split(".mlp.experts.w1")[0]
+            base_key_ops.setdefault(prefix + ".mlp.experts.gate_up_proj", []).append(("even_t", adapter_path))
+        elif ".mlp.experts.w3" in adapter_path:
+            prefix = adapter_path.split(".mlp.experts.w3")[0]
+            base_key_ops.setdefault(prefix + ".mlp.experts.gate_up_proj", []).append(("odd_t", adapter_path))
+        elif ".mlp.experts.w2" in adapter_path:
+            prefix = adapter_path.split(".mlp.experts.w2")[0]
+            base_key_ops.setdefault(prefix + ".mlp.experts.down_proj", []).append(("add_t", adapter_path))
+
+    # Apply LoRA deltas
+    for base_key, ops in tqdm(sorted(base_key_ops.items()), desc="Merging LoRA"):
+        w = state_dict[base_key].float()
+        for op_type, adapter_path in ops:
+            A = lora_pairs[adapter_path]["A"].float()
+            B = lora_pairs[adapter_path]["B"].float()
+            delta = torch.matmul(B, A) * scaling
+            if op_type == "add":
+                w += delta
+            elif op_type == "even_t":
+                w[:, :, ::2] += delta.transpose(1, 2)
+            elif op_type == "odd_t":
+                w[:, :, 1::2] += delta.transpose(1, 2)
+            elif op_type == "add_t":
+                w += delta.transpose(1, 2)
+        state_dict[base_key] = w.to(torch.bfloat16)
+
+    # Save sharded safetensors
+    print(f"Saving to {output_dir}...")
+    max_shard = 5 * 1024**3
+    shards, current, size = [], {}, 0
+    for k, v in state_dict.items():
+        nbytes = v.numel() * v.element_size()
+        if size + nbytes > max_shard and current:
+            shards.append(current)
+            current, size = {}, 0
+        current[k] = v
+        size += nbytes
+    if current:
+        shards.append(current)
+
+    weight_map, total = {}, 0
+    for i, shard in enumerate(shards):
+        fname = f"model-{i+1:05d}-of-{len(shards):05d}.safetensors"
+        save_file(shard, str(output_dir / fname))
+        for k, v in shard.items():
+            weight_map[k] = fname
+            total += v.numel() * v.element_size()
+
+    (output_dir / "model.safetensors.index.json").write_text(
+        json.dumps({"metadata": {"total_size": total}, "weight_map": weight_map}, indent=2)
+    )
+
+    # Copy config + tokenizer from base model (remove quantization_config)
+    base_cache = Path(snapshot_download(BASE_MODEL, allow_patterns=["*.py", "*.json", "tokenizer*", "*.model"]))
+    for f in base_cache.iterdir():
+        if f.is_file() and f.name != "model.safetensors.index.json":
+            shutil.copy2(f, output_dir / f.name)
+    cfg = json.loads((output_dir / "config.json").read_text())
+    cfg.pop("quantization_config", None)
+    cfg["torch_dtype"] = "bfloat16"
+    (output_dir / "config.json").write_text(json.dumps(cfg, indent=2))
+
+    print(f"Done! Merged model saved to {output_dir} ({len(shards)} shards)")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--output", required=True, help="Output directory for merged model")
+    merge(parser.parse_args().output)