add support for model_path to verifiers evaluate script

tinker_cookbook/recipes/verifiers_rl/evaluate.py

@@ -57,22 +57,45 @@ def log_results(
             print(out)
 
 
-def evaluate(
+async def evaluate(
     vf_env_id: str,
     vf_env_args: dict,
-    model_name: str,
+    model_name: str | None,
     num_examples: int,
     rollouts_per_example: int,
     max_concurrent: int,
     max_tokens: int,
     temperature: float,
+    model_path: str | None = None,
 ):
+    service = tinker.ServiceClient()
+
+    # If model_path is provided, get the base model from the training run
+    if model_path is not None:
+        rest_client = service.create_rest_client()
+        training_run = await rest_client.get_training_run_by_tinker_path_async(model_path)
+        if model_name:
+            if model_name != training_run.base_model:
+                raise ValueError(
+                    f"Model name {model_name} does not match training run base model {training_run.base_model}"
+                )
+        else:
+            model_name = training_run.base_model
+
+    if model_name is None:
+        raise ValueError("model_name or model_path must be provided")
+
     env = vf.load_environment(vf_env_id, **vf_env_args)
     tokenizer = get_tokenizer(model_name)
     renderer_name = model_info.get_recommended_renderer_name(model_name)
     renderer = renderers.get_renderer(renderer_name, tokenizer)
-    service = tinker.ServiceClient()
-    sampling = service.create_sampling_client(base_model=model_name)
+
+    # Create sampling client from checkpoint path or base model
+    if model_path:
+        sampling = service.create_sampling_client(model_path=model_path, base_model=model_name)
+    else:
+        sampling = service.create_sampling_client(base_model=model_name)
+
     client = TinkerAsyncOpenAIClient(sampling, renderer, tokenizer)
     start_time = time.time()
     results = env.evaluate_sync(
@@ -95,11 +118,13 @@ def evaluate(
         rollouts_per_example,
         end_time - start_time,
     )
+    return results
 
 
 @chz.chz
 class CLIConfig:
-    model_name: str = "Qwen/Qwen3-4B-Instruct-2507"
+    model_name: str | None = None  # Base model name (auto-detected from checkpoint if not provided)
+    model_path: str | None = None  # Path to checkpoint (e.g., from checkpoints.jsonl sampler_path)
     vf_env_id: str = "reverse-text"
     vf_env_args: str | None = None  # JSON string
     num_examples: int = 5
@@ -111,7 +136,7 @@ class CLIConfig:
 
 async def cli_main(cfg: CLIConfig):
     env_args = json.loads(cfg.vf_env_args) if cfg.vf_env_args else {}
-    return evaluate(
+    return await evaluate(
         vf_env_id=cfg.vf_env_id,
         vf_env_args=env_args,
         model_name=cfg.model_name,
@@ -120,6 +145,7 @@ async def cli_main(cfg: CLIConfig):
         max_concurrent=cfg.max_concurrent,
         max_tokens=cfg.max_tokens,
         temperature=cfg.temperature,
+        model_path=cfg.model_path,
     )