adding convert.py and gitignore to handle model weights and converted npz

.gitignore

@@ -1 +1,4 @@
 .vscode
+llama-weights
+llm-env
+weights.npz

convert.py

@@ -0,0 +1,123 @@
+import numpy as np
+from safetensors import safe_open
+import torch
+import os
+
+# -------- CONFIG --------
+MODEL_DIR = "./llama-weights"   # folder containing all safetensors
+OUT_PATH = "weights.npz"
+
+DTYPE = torch.float16   # use float16 (recommended) or torch.float32
+# ------------------------
+
+# Collect shard files
+files = sorted([f for f in os.listdir(MODEL_DIR) if f.endswith(".safetensors")])
+print("Found shards:", files)
+
+weights = {}
+
+# -------- MERGE FUNCTION --------
+def merge_tensor(key, tensor):
+    if key not in weights:
+        weights[key] = tensor
+    else:
+        # Smart merge rules
+        if tensor.ndim == 2:
+            if any(x in key for x in ["o_proj", "down_proj"]):
+                # concatenate along output dimension
+                weights[key] = np.concatenate([weights[key], tensor], axis=1)
+            else:
+                # default
+                weights[key] = np.concatenate([weights[key], tensor], axis=0)
+
+        elif tensor.ndim == 1:
+            # norms / biases → keep first
+            pass
+
+        else:
+            raise ValueError(f"Unknown tensor shape: {key} {tensor.shape}")
+
+# -------- LOAD SHARDS --------
+for file in files:
+    path = os.path.join(MODEL_DIR, file)
+    print(f"\nLoading {file}")
+
+    with safe_open(path, framework="pt", device="cpu") as f:
+        for key in f.keys():
+            tensor = f.get_tensor(key)
+
+            # ---- dtype fix (bfloat16 → float16/float32) ----
+            if tensor.dtype == torch.bfloat16:
+                tensor = tensor.to(DTYPE)
+            else:
+                tensor = tensor.to(DTYPE)
+
+            tensor = tensor.numpy()
+
+            merge_tensor(key, tensor)
+
+# -------- RENAME KEYS --------
+final_weights = {}
+
+for key, tensor in weights.items():
+    new_key = key
+
+    # embeddings
+    if key == "model.embed_tokens.weight":
+        new_key = "tok_embeddings"
+
+    # final norm
+    elif key == "model.norm.weight":
+        new_key = "norm"
+
+    # output
+    elif key == "lm_head.weight":
+        new_key = "output"
+
+    # layers
+    elif key.startswith("model.layers"):
+        parts = key.split(".")
+        layer_id = parts[2]
+
+        if "self_attn.q_proj.weight" in key:
+            new_key = f"layers.{layer_id}.attention.wq"
+
+        elif "self_attn.k_proj.weight" in key:
+            new_key = f"layers.{layer_id}.attention.wk"
+
+        elif "self_attn.v_proj.weight" in key:
+            new_key = f"layers.{layer_id}.attention.wv"
+
+        elif "self_attn.o_proj.weight" in key:
+            new_key = f"layers.{layer_id}.attention.wo"
+
+        elif "mlp.gate_proj.weight" in key:
+            new_key = f"layers.{layer_id}.feed_forward.w1"
+
+        elif "mlp.down_proj.weight" in key:
+            new_key = f"layers.{layer_id}.feed_forward.w2"
+
+        elif "mlp.up_proj.weight" in key:
+            new_key = f"layers.{layer_id}.feed_forward.w3"
+
+        elif "input_layernorm.weight" in key:
+            new_key = f"layers.{layer_id}.attention_norm"
+
+        elif "post_attention_layernorm.weight" in key:
+            new_key = f"layers.{layer_id}.ffn_norm"
+
+        else:
+            print("Skipping:", key)
+            continue
+
+    else:
+        print("Skipping:", key)
+        continue
+
+    final_weights[new_key] = tensor
+    print(f"{key} -> {new_key} {tensor.shape}")
+
+# -------- SAVE --------
+np.savez(OUT_PATH, **final_weights)
+
+print(f"\n✅ SUCCESS: Saved merged weights to {OUT_PATH}")