""" Inference Script Example =================================== MANDATORY - Before submitting, ensure the following variables are defined in your environment configuration: API_BASE_URL The API endpoint for the LLM. MODEL_NAME The model identifier to use for inference. HF_TOKEN Your Hugging Face / API key. LOCAL_IMAGE_NAME The name of the local image to use for the environment if you are using from_docker_image() method - Defaults are set only for API_BASE_URL and MODEL_NAME (and should reflect your active inference setup): API_BASE_URL = os.getenv("API_BASE_URL", "") MODEL_NAME = os.getenv("MODEL_NAME", "") - The inference script must be named `inference.py` and placed in the root directory of the project - Participants must use OpenAI Client for all LLM calls using above variables STDOUT FORMAT - The script must emit exactly three line types to stdout, in this order: [START] task= env= model= [STEP] step= action= reward=<0.00> done= error= [END] success= steps= score= rewards= Rules: - One [START] line at episode begin. - One [STEP] line per step, immediately after env.step() returns. - One [END] line after env.close(), always emitted (even on exception). - reward and rewards are formatted to 2 decimal places. - done and success are lowercase booleans: true or false. - error is the raw last_action_error string, or null if none. - All fields on a single line with no newlines within a line. - Each tasks should return score in [0, 1] Example: [START] task=click-test env=miniwob model=Qwen3-VL-30B [STEP] step=1 action=click('123') reward=0.00 done=false error=null [STEP] step=2 action=fill('456','text') reward=0.00 done=false error=null [STEP] step=3 action=click('789') reward=1.00 done=true error=null [END] success=true steps=3 score=1.00 rewards=0.00,0.00,1.00 """ import asyncio import os import textwrap from typing import List, Optional from dotenv import load_dotenv load_dotenv() from openai import OpenAI from client import TestEnv from models import TestAction IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME") or os.getenv("IMAGE_NAME") or "test:latest" API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY") API_BASE_URL = os.getenv("API_BASE_URL") or "https://router.huggingface.co/v1" MODEL_NAME = os.getenv("MODEL_NAME") or "Qwen/Qwen2.5-72B-Instruct" TASK_NAME = os.getenv("TEST_TASK", "echo") BENCHMARK = os.getenv("TEST_BENCHMARK", "test") MAX_STEPS = 8 TEMPERATURE = 0.7 MAX_TOKENS = 150 SUCCESS_SCORE_THRESHOLD = 0.1 # normalized score in [0, 1] # Max possible reward: each token contributes 0.1, across all steps _MAX_REWARD_PER_STEP = MAX_TOKENS * 0.1 MAX_TOTAL_REWARD = MAX_STEPS * _MAX_REWARD_PER_STEP SYSTEM_PROMPT = textwrap.dedent( """ You are interacting with a simple echo environment. Each turn you must send a message. The environment will echo it back. Reward is proportional to message length: reward = len(message) * 0.1 Your goal is to maximize total reward by sending meaningful, substantive messages. Reply with exactly one message string — no quotes, no prefixes, just the message text. """ ).strip() def log_start(task: str, env: str, model: str) -> None: print(f"[START] task={task} env={env} model={model}", flush=True) def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str]) -> None: error_val = error if error else "null" done_val = str(done).lower() print( f"[STEP] step={step} action={action} reward={reward:.2f} done={done_val} error={error_val}", flush=True, ) def log_end(success: bool, steps: int, score: float, rewards: List[float]) -> None: rewards_str = ",".join(f"{r:.2f}" for r in rewards) print(f"[END] success={str(success).lower()} steps={steps} score={score:.3f} rewards={rewards_str}", flush=True) def build_user_prompt(step: int, last_echoed: str, last_reward: float, history: List[str]) -> str: history_block = "\n".join(history[-4:]) if history else "None" return textwrap.dedent( f""" Step: {step} Last echoed message: {last_echoed!r} Last reward: {last_reward:.2f} Previous steps: {history_block} Send your next message. """ ).strip() def get_model_message(client: OpenAI, step: int, last_echoed: str, last_reward: float, history: List[str]) -> str: user_prompt = build_user_prompt(step, last_echoed, last_reward, history) try: completion = client.chat.completions.create( model=MODEL_NAME, messages=[ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": user_prompt}, ], temperature=TEMPERATURE, max_tokens=MAX_TOKENS, stream=False, ) text = (completion.choices[0].message.content or "").strip() return text if text else "hello" except Exception as exc: print(f"[DEBUG] Model request failed: {exc}", flush=True) return "hello" async def main() -> None: client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY) env = await TestEnv.from_docker_image(IMAGE_NAME) history: List[str] = [] rewards: List[float] = [] steps_taken = 0 score = 0.0 success = False log_start(task=TASK_NAME, env=BENCHMARK, model=MODEL_NAME) try: result = await env.reset() # OpenENV.reset() last_echoed = result.observation.echoed_message last_reward = 0.0 for step in range(1, MAX_STEPS + 1): if result.done: break message = get_model_message(client, step, last_echoed, last_reward, history) result = await env.step(TestAction(message=message)) obs = result.observation reward = result.reward or 0.0 done = result.done error = None rewards.append(reward) steps_taken = step last_echoed = obs.echoed_message last_reward = reward log_step(step=step, action=message, reward=reward, done=done, error=error) history.append(f"Step {step}: {message!r} -> reward {reward:+.2f}") if done: break score = sum(rewards) / MAX_TOTAL_REWARD if MAX_TOTAL_REWARD > 0 else 0.0 score = min(max(score, 0.0), 1.0) # clamp to [0, 1] success = score >= SUCCESS_SCORE_THRESHOLD finally: try: await env.close() except Exception as e: print(f"[DEBUG] env.close() error (container cleanup): {e}", flush=True) log_end(success=success, steps=steps_taken, score=score, rewards=rewards) if __name__ == "__main__": asyncio.run(main())