Initial chat template debugger - vLLM raw token inspector

scripts/stage1_debug.py

@@ -0,0 +1,70 @@
+"""
+Stage 1: Chat Template Debugger
+
+Runs a raw prompt through vLLM's generate() API — no chat template, no serving
+layer, no middleware. Captures the exact tokens the model emits so you can
+determine whether tool-call failures are a model problem or a parser problem.
+"""
+
+import os
+import json
+from vllm import LLM, SamplingParams
+
+MODEL_PATH = os.environ.get("MODEL_PATH", "/workspace/models/SmolLM3-3B")
+PROMPT_FILE = os.environ.get("PROMPT_FILE", "/workspace/prompts/smol_tool_call.txt")
+MAX_TOKENS = int(os.environ.get("MAX_TOKENS", "512"))
+TEMPERATURE = float(os.environ.get("TEMPERATURE", "0.0"))
+
+# ── Load prompt ──────────────────────────────────────────────────────────────
+
+if os.path.exists(PROMPT_FILE):
+    with open(PROMPT_FILE, "r") as f:
+        prompt = f.read().strip()
+else:
+    print(f"[stage1] Prompt file not found: {PROMPT_FILE}")
+    print("[stage1] Using inline fallback prompt.")
+    prompt = """You are a helpful assistant with access to tools.
+
+Available tools:
+- write_file: Write content to a file. Args: {"path": "string", "content": "string"}
+
+User: Write "hello world" to /tmp/test.txt
+
+Assistant:"""
+
+print(f"[stage1] Prompt ({len(prompt)} chars):\n{'─' * 60}")
+print(prompt)
+print(f"{'─' * 60}\n")
+
+# ── Run model ────────────────────────────────────────────────────────────────
+
+print(f"[stage1] Loading model from {MODEL_PATH} ...")
+llm = LLM(model=MODEL_PATH, trust_remote_code=True)
+
+params = SamplingParams(
+    temperature=TEMPERATURE,
+    max_tokens=MAX_TOKENS,
+)
+
+print(f"[stage1] Generating (temp={TEMPERATURE}, max_tokens={MAX_TOKENS}) ...\n")
+outputs = llm.generate([prompt], params)
+
+# ── Dump results ─────────────────────────────────────────────────────────────
+
+for output in outputs:
+    generated = output.outputs[0]
+    text = generated.text
+    token_ids = list(generated.token_ids)
+
+    print(f"{'═' * 60}")
+    print(f"RAW TEXT:\n{text}")
+    print(f"{'─' * 60}")
+    print(f"TOKEN IDS ({len(token_ids)} tokens):")
+    print(json.dumps(token_ids))
+    print(f"{'─' * 60}")
+    print("PER-TOKEN DECODE:")
+    tokenizer = llm.get_tokenizer()
+    for i, tid in enumerate(token_ids):
+        decoded = tokenizer.decode([tid])
+        print(f"  [{i:4d}] id={tid:>8d}  →  {json.dumps(decoded)}")
+    print(f"{'═' * 60}")