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Model: eivintobias/heartly-qwen-code
Source: Original Platform

chat_smoke.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+"""chat_smoke.py - quick offline test of heartly-qwen-code-v3.
+
+Loads the model once and runs a prompt through the SAME loader + reply_formatter
+that server.py uses, printing RAW (grammar) and REPLY (clean). No HTTP server.
+
+Usage:
+    python chat_smoke.py "Write a function that reverses a string"
+    python chat_smoke.py --mode debug --prompt "Explain a closure"
+    echo "What is a closure?" | python chat_smoke.py
+"""
+from __future__ import annotations
+
+import argparse
+import sys
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from reply_formatter import format_reply
+
+
+def main():
+    p = argparse.ArgumentParser(description="Smoke-test heartly-qwen-code-v3")
+    p.add_argument("prompt_pos", nargs="?", default=None,
+                   help="question to ask (positional, e.g. chat_smoke.py 'reverse a list')")
+    p.add_argument("--prompt", "-p", dest="prompt_opt", default=None,
+                   help="question to ask (alternative to positional)")
+    p.add_argument("--model", default="heartly-qwen-code-v3")
+    p.add_argument("--mode", default="chat", choices=["chat", "debug", "raw"])
+    p.add_argument("--max-new-tokens", type=int, default=200)
+    a = p.parse_args()
+
+    # Precedence: --prompt > positional > stdin > hard-coded default.
+    if a.prompt_opt is not None:
+        prompt = a.prompt_opt
+    elif a.prompt_pos is not None:
+        prompt = a.prompt_pos
+    else:
+        prompt = sys.stdin.read().strip()
+    if not prompt:
+        prompt = "Write a function that reverses a string"  # safety default
+
+    print(f"Loading {a.model} ...", file=sys.stderr)
+    tok = AutoTokenizer.from_pretrained(a.model)
+    model = AutoModelForCausalLM.from_pretrained(
+        a.model, torch_dtype=torch.float32, device_map="cpu"
+    ).eval()
+
+    ids = tok.encode(f"User: {prompt}\nAssistant: ", return_tensors="pt")
+    with torch.no_grad():
+        out = model.generate(
+            ids,
+            max_new_tokens=a.max_new_tokens,
+            pad_token_id=tok.eos_token_id,
+            do_sample=False,
+        )
+    raw = tok.decode(out[0][ids.shape[1]:], skip_special_tokens=False)
+    reply = format_reply(raw, mode=a.mode)
+
+    print("\n=== RAW ===")
+    print(raw)
+    print("\n=== REPLY (" + a.mode + ") ===")
+    print(reply)
+
+
+if __name__ == "__main__":
+    main()