Update clean backend-only project docs and eval

scripts/export_production.py

@@ -18,6 +18,7 @@ import subprocess
 import sys
 import urllib.request
 from pathlib import Path
+from typing import Any, cast
 
 REPO_ROOT = Path(__file__).resolve().parents[1]
 DEFAULT_CHECKPOINT = REPO_ROOT / "checkpoints" / "blitzkode-1.5b-lora" / "final"
@@ -60,7 +61,7 @@ def merge_adapter(checkpoint: Path, merged_dir: Path) -> None:
     print(f"  Checkpoint : {checkpoint}")
     print(f"  Base model : {base_name}")
 
-    dtype = torch.float16
+    dtype = cast(Any, torch).float16
     print("  Loading base model …")
     base = AutoModelForCausalLM.from_pretrained(
         base_name,
@@ -71,7 +72,7 @@ def merge_adapter(checkpoint: Path, merged_dir: Path) -> None:
     tokenizer = AutoTokenizer.from_pretrained(str(checkpoint), trust_remote_code=True)
 
     print("  Loading & merging adapter …")
-    model = PeftModel.from_pretrained(base, str(checkpoint))
+    model = cast(Any, PeftModel.from_pretrained(base, str(checkpoint)))
     model = model.merge_and_unload()
 
     merged_dir.mkdir(parents=True, exist_ok=True)
@@ -140,7 +141,7 @@ def verify_gguf(gguf_path: Path) -> None:
             verbose=False,
         )
         prompt = "<|im_start|>user\nSay hello.<|im_end|>\n<|im_start|>assistant\n"
-        out = llm(prompt, max_tokens=8, stop=["<|im_end|>"])
+        out = cast(dict[str, Any], llm(prompt, max_tokens=8, stop=["<|im_end|>"]))
         text = out["choices"][0]["text"].strip()
         print(f"  Sample output: {text!r}")
         print("  Verification PASSED.")