diff --git a/dev/playwright_agent/playwright_agent.py b/dev/playwright_agent/playwright_agent.py
index 867eb2103..3493e893d 100644
--- a/dev/playwright_agent/playwright_agent.py
+++ b/dev/playwright_agent/playwright_agent.py
@@ -4,13 +4,13 @@
 
 import argparse
 import asyncio
+from dataclasses import dataclass
 import json
 import math
 import os
 import time
-import uuid
-from dataclasses import dataclass
 from typing import Any, Dict, List
+import uuid
 
 from dotenv import load_dotenv
 from mcp import ClientSession, StdioServerParameters
diff --git a/dev/swebench/logs.py b/dev/swebench/logs.py
index 8cec9359f..bedf963ad 100644
--- a/dev/swebench/logs.py
+++ b/dev/swebench/logs.py
@@ -1,10 +1,10 @@
 import logging
 from logging import Handler, LogRecord
 
-import litellm
 from langfuse import Langfuse
 from langfuse.decorators import langfuse_context
 from langfuse.types import SpanLevel
+import litellm
 from sweagent.agent.agents import DefaultAgent
 from sweagent.run.hooks.apply_patch import SaveApplyPatchHook
 
diff --git a/dev/swebench/rollout.py b/dev/swebench/rollout.py
index 2622d7b01..c0b1ed184 100644
--- a/dev/swebench/rollout.py
+++ b/dev/swebench/rollout.py
@@ -1,11 +1,9 @@
 import asyncio
-import json
 from http.client import RemoteDisconnected
+import json
 from pathlib import Path
 from typing import Any, Literal, overload
 
-import modal
-import requests
 from aiolimiter import AsyncLimiter
 from config import get_config
 from eval import eval_instance
@@ -13,7 +11,9 @@
 from instances import Instance
 from langfuse.decorators import observe
 from logs import setup_agent_logger
+import modal
 from pydantic import BaseModel
+import requests
 from requests import adapters as requests_adapters
 from requests.exceptions import ConnectionError, ConnectTimeout, SSLError
 from run import run
diff --git a/dev/swebench/sandbox/test.py b/dev/swebench/sandbox/test.py
index 371e36fe7..9c210b5aa 100644
--- a/dev/swebench/sandbox/test.py
+++ b/dev/swebench/sandbox/test.py
@@ -1,5 +1,5 @@
-import pytest
 from dotenv import load_dotenv
+import pytest
 
 from ..instances import as_instances_iter, get_filtered_swe_smith_instances_df
 from .new import new_sandbox
diff --git a/dev/swebench/train.ipynb b/dev/swebench/train.ipynb
index 2f96b1ef5..44f2fed2b 100644
--- a/dev/swebench/train.ipynb
+++ b/dev/swebench/train.ipynb
@@ -36,8 +36,8 @@
    "source": [
     "import os\n",
     "\n",
-    "import nest_asyncio\n",
     "from dotenv import load_dotenv\n",
+    "import nest_asyncio\n",
     "\n",
     "# PREPARE ENVIRONMENT\n",
     "os.environ[\"MODAL_IMAGE_BUILDER_VERSION\"] = \"2024.10\"\n",
@@ -60,8 +60,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import torch\n",
     "from rollout import ModelConfig\n",
+    "import torch\n",
     "\n",
     "import art\n",
     "from art.local import LocalBackend\n",
diff --git a/dev/tau-bench/analyze_model_errors.py b/dev/tau-bench/analyze_model_errors.py
index 7bde8743a..14b86549d 100644
--- a/dev/tau-bench/analyze_model_errors.py
+++ b/dev/tau-bench/analyze_model_errors.py
@@ -2,13 +2,13 @@
 
 import argparse
 import asyncio
+from datetime import datetime
 import json
 import os
-from datetime import datetime
 from typing import Any, Dict, List
 
-import litellm
 from dotenv import load_dotenv
+import litellm
 from litellm import provider_list
 
 # Import evaluate_model and rollout functions from run_rl
diff --git a/dev/tau-bench/auto_error_identification.py b/dev/tau-bench/auto_error_identification.py
index ecf422f46..09dbfd90f 100644
--- a/dev/tau-bench/auto_error_identification.py
+++ b/dev/tau-bench/auto_error_identification.py
@@ -1,9 +1,9 @@
 # Copyright Sierra
 
 import argparse
-import json
 from concurrent.futures import ThreadPoolExecutor
 from enum import Enum
+import json
 from typing import Any, Dict, List
 
 from pydantic import BaseModel
diff --git a/dev/tau-bench/benchmark_rl.py b/dev/tau-bench/benchmark_rl.py
index fa318970d..2feef2069 100644
--- a/dev/tau-bench/benchmark_rl.py
+++ b/dev/tau-bench/benchmark_rl.py
@@ -2,13 +2,13 @@
 
 import argparse
 import asyncio
+from datetime import datetime
 import json
 import os
-from datetime import datetime
 from typing import Any, Dict, List
 
-import litellm
 from dotenv import load_dotenv
+import litellm
 from litellm import provider_list
 
 # Import evaluate_model and rollout functions from run_rl
diff --git a/dev/tau-bench/run.ipynb b/dev/tau-bench/run.ipynb
index a55ce6668..1872352dc 100644
--- a/dev/tau-bench/run.ipynb
+++ b/dev/tau-bench/run.ipynb
@@ -37,11 +37,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import torch\n",
     "from dotenv import load_dotenv\n",
     "from run import RunConfig\n",
     "from run_rl import train\n",
     "from tau_bench.types import TauBenchPolicyConfig, TauBenchTrainingConfig\n",
+    "import torch\n",
     "\n",
     "import art\n",
     "\n",
diff --git a/dev/tau-bench/run_training.py b/dev/tau-bench/run_training.py
index bae334fa0..3ac733933 100644
--- a/dev/tau-bench/run_training.py
+++ b/dev/tau-bench/run_training.py
@@ -4,8 +4,8 @@
 import textwrap
 import traceback
 
-import sky
 from dotenv import dotenv_values
+import sky
 from sky import ClusterStatus
 from tau_bench.types import RunConfig, TauBenchPolicyConfig, TauBenchTrainingConfig
 
diff --git a/dev/tau-bench/tau_bench/envs/airline/tools/book_reservation.py b/dev/tau-bench/tau_bench/envs/airline/tools/book_reservation.py
index f4195b8ab..2f342492c 100644
--- a/dev/tau-bench/tau_bench/envs/airline/tools/book_reservation.py
+++ b/dev/tau-bench/tau_bench/envs/airline/tools/book_reservation.py
@@ -1,7 +1,7 @@
 # Copyright Sierra
 
-import json
 from copy import deepcopy
+import json
 from typing import Any, Dict, List
 
 from tau_bench.envs.tool import Tool
diff --git a/dev/tau-bench/tau_bench/envs/airline/tools/update_reservation_flights.py b/dev/tau-bench/tau_bench/envs/airline/tools/update_reservation_flights.py
index b31fa92cd..ffd050190 100644
--- a/dev/tau-bench/tau_bench/envs/airline/tools/update_reservation_flights.py
+++ b/dev/tau-bench/tau_bench/envs/airline/tools/update_reservation_flights.py
@@ -1,7 +1,7 @@
 # Copyright Sierra
 
-import json
 from copy import deepcopy
+import json
 from typing import Any, Dict, List
 
 from tau_bench.envs.tool import Tool
diff --git a/dev/tau-bench/tau_bench/envs/base.py b/dev/tau-bench/tau_bench/envs/base.py
index a54bc5226..54c035e16 100644
--- a/dev/tau-bench/tau_bench/envs/base.py
+++ b/dev/tau-bench/tau_bench/envs/base.py
@@ -1,7 +1,7 @@
 # Copyright Sierra
 
-import random
 from hashlib import sha256
+import random
 from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
 
 from tau_bench.envs.tool import Tool
diff --git a/dev/tau-bench/tau_bench/model_utils/api/cache.py b/dev/tau-bench/tau_bench/model_utils/api/cache.py
index 2735eed3e..e8bed50d4 100644
--- a/dev/tau-bench/tau_bench/model_utils/api/cache.py
+++ b/dev/tau-bench/tau_bench/model_utils/api/cache.py
@@ -1,9 +1,9 @@
+from collections import defaultdict
 import functools
 import hashlib
 import inspect
-import threading
-from collections import defaultdict
 from multiprocessing import Lock
+import threading
 from typing import Any, Callable, TypeVar
 
 from pydantic import BaseModel
diff --git a/dev/tau-bench/tau_bench/model_utils/api/exception.py b/dev/tau-bench/tau_bench/model_utils/api/exception.py
index 40b424514..73713b75a 100644
--- a/dev/tau-bench/tau_bench/model_utils/api/exception.py
+++ b/dev/tau-bench/tau_bench/model_utils/api/exception.py
@@ -1,7 +1,7 @@
+from concurrent.futures import ThreadPoolExecutor
 import json
 import os
 import time
-from concurrent.futures import ThreadPoolExecutor
 from typing import Any, Callable, TypeVar
 
 from tau_bench.model_utils.model.exception import ModelError, Result
diff --git a/dev/tau-bench/tau_bench/rl_utils.py b/dev/tau-bench/tau_bench/rl_utils.py
index b94a6d77c..ce0f31c98 100644
--- a/dev/tau-bench/tau_bench/rl_utils.py
+++ b/dev/tau-bench/tau_bench/rl_utils.py
@@ -1,8 +1,8 @@
+from datetime import datetime
 import os
 import time
-import uuid
-from datetime import datetime
 from typing import Any, Dict, List, Optional
+import uuid
 
 from langfuse import Langfuse
 from openai import AsyncOpenAI
diff --git a/dev/tau-bench/tau_bench/run.py b/dev/tau-bench/tau_bench/run.py
index 663b2ad76..987506f3f 100644
--- a/dev/tau-bench/tau_bench/run.py
+++ b/dev/tau-bench/tau_bench/run.py
@@ -1,14 +1,14 @@
 # Copyright Sierra
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime
 import json
+from math import comb
 import multiprocessing
 import os
 import random
 import traceback
-import warnings
-from concurrent.futures import ThreadPoolExecutor
-from datetime import datetime
-from math import comb
 from typing import Any, Dict, List
+import warnings
 
 from langfuse import Langfuse
 from litellm import provider_list
diff --git a/dev/yes-no-maybe-vision/train.ipynb b/dev/yes-no-maybe-vision/train.ipynb
index 46d29c726..939c47aca 100644
--- a/dev/yes-no-maybe-vision/train.ipynb
+++ b/dev/yes-no-maybe-vision/train.ipynb
@@ -34,9 +34,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import openai\n",
     "from dotenv import load_dotenv\n",
     "from generate_images import generate_yes_no_maybe_prompts, save_prompt_images\n",
+    "import openai\n",
     "\n",
     "import art\n",
     "from art.local import LocalBackend\n",
diff --git a/dev/yes-no-maybe.ipynb b/dev/yes-no-maybe.ipynb
index b18d83265..11e060b93 100644
--- a/dev/yes-no-maybe.ipynb
+++ b/dev/yes-no-maybe.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,30 +12,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       ".cell-output-ipywidget-background {\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       ":root {\n",
-       "    --jp-widgets-color: var(--vscode-editor-foreground);\n",
-       "    --jp-widgets-font-size: var(--vscode-editor-font-size);\n",
-       "}  \n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%html\n",
     "<style>\n",
@@ -57,8 +36,8 @@
    "source": [
     "from itertools import permutations\n",
     "\n",
-    "import openai\n",
     "from dotenv import load_dotenv\n",
+    "import openai\n",
     "\n",
     "import art\n",
     "from art.local import LocalBackend\n",
@@ -67,7 +46,7 @@
     "\n",
     "backend = LocalBackend()\n",
     "model = art.TrainableModel(\n",
-    "    name=\"009\",\n",
+    "    name=\"010\",\n",
     "    project=\"yes-no-maybe\",\n",
     "    base_model=\"Qwen/Qwen2.5-7B-Instruct\",\n",
     "    # _internal_config=art.dev.InternalModelConfig(\n",
@@ -149,7 +128,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.18"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/dev/yes-no-maybe.py b/dev/yes-no-maybe.py
new file mode 100644
index 000000000..bf196e016
--- /dev/null
+++ b/dev/yes-no-maybe.py
@@ -0,0 +1,90 @@
+import asyncio
+from itertools import permutations
+import os
+
+from dotenv import load_dotenv
+import openai
+
+import art
+from art.local import LocalBackend
+
+
+async def rollout(client: openai.AsyncOpenAI, prompt: str) -> art.Trajectory:
+    messages: art.Messages = [
+        {
+            "role": "user",
+            "content": prompt,
+        }
+    ]
+    chat_completion = await client.chat.completions.create(
+        messages=messages, model=model.name, max_tokens=100, timeout=100
+    )
+    choice = chat_completion.choices[0]
+    content = choice.message.content
+    assert isinstance(content, str)
+    if content == "yes":
+        reward = 0.5
+    elif content == "no":
+        reward = 0.75
+    elif content == "maybe":
+        reward = 1.0
+    else:
+        reward = 0.0
+    return art.Trajectory(messages_and_choices=[*messages, choice], reward=reward)
+
+
+def with_quotes(w: str) -> str:
+    return f"'{w}'"
+
+
+async def main():
+    load_dotenv()
+
+    backend = LocalBackend()
+    global model
+    base_model = os.environ.get("BASE_MODEL", "Qwen/Qwen3-30B-A3B-Instruct-2507")
+    model = art.TrainableModel(
+        name=os.environ.get("MODEL_NAME", "011"),
+        project="yes-no-maybe",
+        base_model=base_model,
+        _internal_config=art.dev.InternalModelConfig(
+            engine_args=art.dev.EngineArgs(
+                max_lora_rank=1,
+            ),
+            peft_args=art.dev.PeftArgs(
+                r=1,
+            ),
+        ),
+    )
+    await model.register(backend)
+
+    prompts = [
+        f"{prefix} with {', '.join([with_quotes(w) if use_quotes else w for w in words]) if len(words) == 3 else f'{words[0]}' + (f' or {words[1]}' if len(words) > 1 else '')}"
+        for prefix in ["respond", "just respond"]
+        for use_quotes in [True, False]
+        for words in (
+            list(p) for n in [3, 2] for p in permutations(["yes", "no", "maybe"], n)
+        )
+    ]
+
+    openai_client = model.openai_client()
+    max_steps = int(os.environ.get("NUM_STEPS", "4"))
+    start_step = await model.get_step()
+    for _ in range(start_step, start_step + max_steps):
+        train_groups = await art.gather_trajectory_groups(
+            (
+                art.TrajectoryGroup(rollout(openai_client, prompt) for _ in range(32))
+                for prompt in prompts
+            )
+        )
+        await model.train(
+            train_groups,
+            config=art.TrainConfig(learning_rate=1e-4),
+            # _config=art.dev.TrainConfig(
+            #     precalculate_logprobs=True,
+            # ),
+        )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/examples/2048/rollout.py b/examples/2048/rollout.py
index 3179aaf18..1cb2828ef 100644
--- a/examples/2048/rollout.py
+++ b/examples/2048/rollout.py
@@ -2,10 +2,9 @@
 import math
 import os
 
+from dotenv import load_dotenv
 import openai
 import requests
-import weave
-from dotenv import load_dotenv
 from utils import (
     WINNING_VALUE,
     apply_agent_move,
@@ -15,6 +14,7 @@
     render_board,
     total_board_value,
 )
+import weave
 
 import art
 
diff --git a/examples/2048/utils.py b/examples/2048/utils.py
index 97649e8e3..da6738f5a 100644
--- a/examples/2048/utils.py
+++ b/examples/2048/utils.py
@@ -1,8 +1,8 @@
+from enum import Enum
 import random
 import string
-import xml.etree.ElementTree as ET
-from enum import Enum
 from typing import TypedDict
+import xml.etree.ElementTree as ET
 
 from dotenv import load_dotenv
 
diff --git a/examples/hn_title_generator/reference_grpo_trainer.py b/examples/hn_title_generator/reference_grpo_trainer.py
index 715846dd7..977da10d9 100644
--- a/examples/hn_title_generator/reference_grpo_trainer.py
+++ b/examples/hn_title_generator/reference_grpo_trainer.py
@@ -2,22 +2,17 @@
 import os
 from typing import Tuple
 
-import numpy as np
 from datasets import Dataset
 from dotenv import load_dotenv
-from transformers import PreTrainedTokenizer
+import numpy as np
+from transformers.tokenization_utils import PreTrainedTokenizer
 from transformers.trainer_callback import TrainerCallback
 from trl import GRPOConfig, GRPOTrainer
 from unsloth import FastLanguageModel, is_bfloat16_supported
-from utils import (
-    cache,
-    prompt_for_title,
-    pull_data,
-    score_title,
-)
+from utils import cache, prompt_for_title, pull_data, score_title
 from vllm import SamplingParams
-
 import wandb
+
 from art.utils import limit_concurrency
 
 load_dotenv()
diff --git a/examples/hn_title_generator/train.py b/examples/hn_title_generator/train.py
index bc0aba50b..b179263a8 100644
--- a/examples/hn_title_generator/train.py
+++ b/examples/hn_title_generator/train.py
@@ -1,11 +1,11 @@
 import asyncio
-import os
 from datetime import datetime
+import os
 from typing import Any, Dict, Iterable, List
 
-import openai
 from datasets import Dataset
 from dotenv import load_dotenv
+import openai
 from openai.types.chat import ChatCompletionMessageParam
 from openpipe import AsyncOpenPipe
 from transformers.models.auto.tokenization_auto import AutoTokenizer
diff --git a/examples/hn_title_generator/utils.py b/examples/hn_title_generator/utils.py
index 9dafaec5d..9f6038f6d 100644
--- a/examples/hn_title_generator/utils.py
+++ b/examples/hn_title_generator/utils.py
@@ -1,13 +1,13 @@
+from datetime import datetime
 import math
 import os
-from datetime import datetime
 from typing import Dict, Optional
 
-import httpx
-import polars as pl
 from datasets import Dataset, load_dataset
 from dotenv import load_dotenv
+import httpx
 from panza import SQLiteCache
+import polars as pl
 from pydantic import BaseModel, Field, field_serializer
 
 load_dotenv()
diff --git a/examples/just-the-facts/just_the_facts/checks.py b/examples/just-the-facts/just_the_facts/checks.py
index ddcd7dafa..606ff23d9 100644
--- a/examples/just-the-facts/just_the_facts/checks.py
+++ b/examples/just-the-facts/just_the_facts/checks.py
@@ -1,9 +1,9 @@
 import json
 import os
 
-import tenacity
 from dotenv import load_dotenv
 from openai import AsyncOpenAI
+import tenacity
 
 load_dotenv()
 
diff --git a/examples/just-the-facts/just_the_facts/display_benchmarks.ipynb b/examples/just-the-facts/just_the_facts/display_benchmarks.ipynb
index c340a069b..29aa64113 100644
--- a/examples/just-the-facts/just_the_facts/display_benchmarks.ipynb
+++ b/examples/just-the-facts/just_the_facts/display_benchmarks.ipynb
@@ -150,8 +150,8 @@
     "import importlib\n",
     "import os\n",
     "\n",
-    "import polars as pl\n",
     "from dotenv import load_dotenv\n",
+    "import polars as pl\n",
     "\n",
     "import art.utils.benchmarking.charts\n",
     "from art.utils.benchmarking.charts import (\n",
diff --git a/examples/just-the-facts/just_the_facts/rollout.py b/examples/just-the-facts/just_the_facts/rollout.py
index c7f85ab03..df01a931e 100644
--- a/examples/just-the-facts/just_the_facts/rollout.py
+++ b/examples/just-the-facts/just_the_facts/rollout.py
@@ -1,10 +1,10 @@
 import asyncio
 import os
 
-import weave
 from dotenv import load_dotenv
 from openai import AsyncOpenAI
 from pydantic import BaseModel
+import weave
 
 import art
 from just_the_facts.checks import (
diff --git a/examples/just-the-facts/just_the_facts/train.py b/examples/just-the-facts/just_the_facts/train.py
index d01c32421..3941c7a9b 100644
--- a/examples/just-the-facts/just_the_facts/train.py
+++ b/examples/just-the-facts/just_the_facts/train.py
@@ -1,9 +1,9 @@
 import asyncio
 import os
 
-import weave
 from dotenv import load_dotenv
 from rollout import rollout
+import weave
 
 import art
 from art.utils import iterate_dataset
diff --git a/examples/just-the-facts/run_remote.py b/examples/just-the-facts/run_remote.py
index 34dfc5b95..ba5f3d018 100644
--- a/examples/just-the-facts/run_remote.py
+++ b/examples/just-the-facts/run_remote.py
@@ -2,11 +2,11 @@
 # uv run run_training.py --models=207 --fast
 
 import argparse
-import textwrap
 from datetime import datetime
+import textwrap
 
-import sky
 from dotenv import dotenv_values, load_dotenv
+import sky
 from sky import ClusterStatus
 
 load_dotenv()
diff --git a/examples/mcp-rl/mcp_rl/benchmarks/generate_benchmarks.py b/examples/mcp-rl/mcp_rl/benchmarks/generate_benchmarks.py
index 84bd53d16..7b039ab0f 100644
--- a/examples/mcp-rl/mcp_rl/benchmarks/generate_benchmarks.py
+++ b/examples/mcp-rl/mcp_rl/benchmarks/generate_benchmarks.py
@@ -5,7 +5,6 @@
 import random
 from typing import List
 
-import weave
 from dotenv import load_dotenv
 from servers.python.mcp_alphavantage.server_params import (
     server_params as alphavantage_server_params,
@@ -13,6 +12,7 @@
 from servers.python.mcp_balldontlie.server_params import (
     server_params as balldontlie_server_params,
 )
+import weave
 
 import art
 from art.local import LocalBackend
diff --git a/examples/mcp-rl/mcp_rl/checks.py b/examples/mcp-rl/mcp_rl/checks.py
index 73bd60c7f..7d0d846ed 100644
--- a/examples/mcp-rl/mcp_rl/checks.py
+++ b/examples/mcp-rl/mcp_rl/checks.py
@@ -3,10 +3,10 @@
 import json
 import os
 
-import weave
 from dotenv import load_dotenv
 from openai import AsyncOpenAI
 from tenacity import retry, stop_after_attempt, wait_exponential
+import weave
 
 import art
 from art.utils import limit_concurrency
diff --git a/examples/mcp-rl/mcp_rl/mcp_server.py b/examples/mcp-rl/mcp_rl/mcp_server.py
index 74e3bb28d..f319dfc75 100644
--- a/examples/mcp-rl/mcp_rl/mcp_server.py
+++ b/examples/mcp-rl/mcp_rl/mcp_server.py
@@ -1,8 +1,8 @@
 """MCP Server wrapper class for integration with ART."""
 
+from abc import ABC, abstractmethod
 import json
 import ssl
-from abc import ABC, abstractmethod
 from typing import Any, Dict, List, Optional
 
 import aiohttp
diff --git a/examples/mcp-rl/mcp_rl/rollout.py b/examples/mcp-rl/mcp_rl/rollout.py
index 86d2d1909..c7dc35923 100644
--- a/examples/mcp-rl/mcp_rl/rollout.py
+++ b/examples/mcp-rl/mcp_rl/rollout.py
@@ -5,17 +5,17 @@
 """
 
 import asyncio
+from dataclasses import dataclass
 import json
 import logging
 import os
-from dataclasses import dataclass
 
-import weave
 from dotenv import load_dotenv
 from mcp import ClientSession, StdioServerParameters
 from mcp.client.stdio import stdio_client
 from openai import AsyncOpenAI
 from servers.python.mcp_alphavantage.server_params import server_params
+import weave
 
 import art
 
diff --git a/examples/mcp-rl/mcp_rl/scenario_generator.py b/examples/mcp-rl/mcp_rl/scenario_generator.py
index 0908d115e..b9479bbe0 100644
--- a/examples/mcp-rl/mcp_rl/scenario_generator.py
+++ b/examples/mcp-rl/mcp_rl/scenario_generator.py
@@ -8,10 +8,10 @@
 import random
 from typing import Any, Dict, List
 
-import openai
 from dotenv import load_dotenv
 from mcp import ClientSession
 from mcp.client.stdio import stdio_client
+import openai
 
 load_dotenv()
 
diff --git a/examples/mcp-rl/mcp_rl/train.py b/examples/mcp-rl/mcp_rl/train.py
index 98972103d..3417423d2 100644
--- a/examples/mcp-rl/mcp_rl/train.py
+++ b/examples/mcp-rl/mcp_rl/train.py
@@ -6,8 +6,8 @@
 import json
 import os
 
-import weave
 from dotenv import load_dotenv
+import weave
 
 import art
 from art.rewards import ruler_score_group
diff --git a/examples/mcp-rl/run_remote.py b/examples/mcp-rl/run_remote.py
index 70da3024b..0705f02a2 100644
--- a/examples/mcp-rl/run_remote.py
+++ b/examples/mcp-rl/run_remote.py
@@ -2,11 +2,11 @@
 # uv run run_training.py --models=207 --fast
 
 import argparse
-import textwrap
 from datetime import datetime
+import textwrap
 
-import sky
 from dotenv import dotenv_values, load_dotenv
+import sky
 from sky import ClusterStatus
 
 load_dotenv()
diff --git a/examples/mcp-rl/servers/python/mcp_alphavantage/server.py b/examples/mcp-rl/servers/python/mcp_alphavantage/server.py
index 7a51fab8d..fc735fc1e 100644
--- a/examples/mcp-rl/servers/python/mcp_alphavantage/server.py
+++ b/examples/mcp-rl/servers/python/mcp_alphavantage/server.py
@@ -3,8 +3,8 @@
 
 import aiohttp
 import click
-import mcp.types as types
 from mcp.server.lowlevel import Server
+import mcp.types as types
 from tenacity import (
     retry,
     retry_if_exception_type,
diff --git a/examples/mcp-rl/servers/python/mcp_balldontlie/server.py b/examples/mcp-rl/servers/python/mcp_balldontlie/server.py
index feac25be6..93a884f8d 100644
--- a/examples/mcp-rl/servers/python/mcp_balldontlie/server.py
+++ b/examples/mcp-rl/servers/python/mcp_balldontlie/server.py
@@ -3,8 +3,8 @@
 
 import aiohttp
 import click
-import mcp.types as types
 from mcp.server.lowlevel import Server
+import mcp.types as types
 from tenacity import (
     retry,
     retry_if_exception_type,
diff --git a/examples/mcp-rl/servers/python/mcp_googlemaps/server.py b/examples/mcp-rl/servers/python/mcp_googlemaps/server.py
index a1a037c0d..c4f404497 100644
--- a/examples/mcp-rl/servers/python/mcp_googlemaps/server.py
+++ b/examples/mcp-rl/servers/python/mcp_googlemaps/server.py
@@ -3,8 +3,8 @@
 
 import aiohttp
 import click
-import mcp.types as types
 from mcp.server.lowlevel import Server
+import mcp.types as types
 from tenacity import (
     retry,
     retry_if_exception_type,
diff --git a/examples/openenv_echo.py b/examples/openenv_echo.py
index 3c1003635..8da3e8af9 100644
--- a/examples/openenv_echo.py
+++ b/examples/openenv_echo.py
@@ -9,9 +9,9 @@
 import asyncio
 from datetime import datetime
 
-import weave
 from dotenv import load_dotenv
 from envs.echo_env import EchoAction, EchoEnv
+import weave
 
 import art
 from art.serverless.backend import ServerlessBackend
diff --git a/examples/temporal_clue/temporal-clue-torchtune.ipynb b/examples/temporal_clue/temporal-clue-torchtune.ipynb
index afb46b126..041e7315c 100644
--- a/examples/temporal_clue/temporal-clue-torchtune.ipynb
+++ b/examples/temporal_clue/temporal-clue-torchtune.ipynb
@@ -60,8 +60,8 @@
     "import re\n",
     "from typing import TypedDict\n",
     "\n",
-    "import torch\n",
     "from dotenv import load_dotenv\n",
+    "import torch\n",
     "\n",
     "import art\n",
     "from art.local import LocalBackend\n",
diff --git a/examples/tic_tac_toe/game_utils.py b/examples/tic_tac_toe/game_utils.py
index 48cfefce6..96fcf5d9f 100644
--- a/examples/tic_tac_toe/game_utils.py
+++ b/examples/tic_tac_toe/game_utils.py
@@ -1,6 +1,6 @@
 import random
-import xml.etree.ElementTree as ET
 from typing import Literal, TypedDict
+import xml.etree.ElementTree as ET
 
 
 class TicTacToeGame(TypedDict):
diff --git a/examples/tic_tac_toe/rollout.py b/examples/tic_tac_toe/rollout.py
index 941290e25..3c9c0ad43 100644
--- a/examples/tic_tac_toe/rollout.py
+++ b/examples/tic_tac_toe/rollout.py
@@ -2,8 +2,6 @@
 import os
 import time
 
-import openai
-import weave
 from dotenv import load_dotenv
 from game_utils import (
     apply_agent_move,
@@ -12,7 +10,9 @@
     get_opponent_move,
     render_board,
 )
+import openai
 from pydantic import BaseModel
+import weave
 
 import art
 
diff --git a/examples/tic_tac_toe/tic-tac-toe.py b/examples/tic_tac_toe/tic-tac-toe.py
index 72d3b7594..65916f81e 100644
--- a/examples/tic_tac_toe/tic-tac-toe.py
+++ b/examples/tic_tac_toe/tic-tac-toe.py
@@ -3,9 +3,9 @@
 import os
 import random
 
-import weave
 from dotenv import load_dotenv
 from rollout import TicTacToeScenario, rollout
+import weave
 
 import art
 from art.utils.deployment import TogetherDeploymentConfig, deploy_model
diff --git a/examples/tic_tac_toe_self_play/game_utils.py b/examples/tic_tac_toe_self_play/game_utils.py
index b15420521..86d256792 100644
--- a/examples/tic_tac_toe_self_play/game_utils.py
+++ b/examples/tic_tac_toe_self_play/game_utils.py
@@ -1,5 +1,5 @@
-import xml.etree.ElementTree as ET
 from typing import Literal, TypedDict
+import xml.etree.ElementTree as ET
 
 
 class TicTacToeGame(TypedDict):
diff --git a/examples/tic_tac_toe_self_play/rollout.py b/examples/tic_tac_toe_self_play/rollout.py
index 658a405b1..ff09b4544 100644
--- a/examples/tic_tac_toe_self_play/rollout.py
+++ b/examples/tic_tac_toe_self_play/rollout.py
@@ -2,8 +2,6 @@
 import os
 import time
 
-import openai
-import weave
 from dotenv import load_dotenv
 from game_utils import (
     TicTacToeGame,
@@ -13,8 +11,10 @@
     render_board,
     unwrap_move,
 )
+import openai
 from openai.types.chat import ChatCompletion
 from pydantic import BaseModel
+import weave
 
 import art
 from art.guided_completion import get_guided_completion_params
diff --git a/examples/tic_tac_toe_self_play/train.py b/examples/tic_tac_toe_self_play/train.py
index 3e3d67710..fc615e243 100644
--- a/examples/tic_tac_toe_self_play/train.py
+++ b/examples/tic_tac_toe_self_play/train.py
@@ -3,11 +3,11 @@
 import os
 import random
 
-import weave
 from dotenv import load_dotenv
 from game_utils import possible_moves
 from gather_trajectory_groups_by_index import gather_trajectory_groups_by_index
 from rollout import ModelConfig, TicTacToeScenario, rollout
+import weave
 
 import art
 from art.utils.strip_logprobs import strip_logprobs
diff --git a/pyproject.toml b/pyproject.toml
index 737c8cac8..4e1990d62 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,13 +18,12 @@ backend = [
     "peft>=0.14.0",
     "hf-xet>=1.1.0",
     "bitsandbytes>=0.45.2",
-    "unsloth==2025.10.3",
-    "unsloth-zoo==2025.10.3",
-    "vllm>=0.9.2,<=0.10.0",
+    "unsloth==2025.12.9",
+    "unsloth-zoo==2025.12.7",
+    "vllm>=0.11.0",
     "torchtune",
-    "trl>=0.19.0",
-    "torch>=2.7.0",
-    "torchao>=0.9.0",
+    "torch>=2.8.0",
+    "torchao==0.14.1",
     "accelerate==1.7.0",
     "awscli>=1.38.1",
     "setproctitle>=1.3.6",
@@ -32,9 +31,9 @@ backend = [
     "setuptools>=78.1.0",
     "wandb==0.22.1",
     "polars>=1.26.0",
+    "transformers>=4.55.2,<=4.57.3",
     "duckdb>=1.0.0",
     "pyarrow>=15.0.0",
-    "transformers==4.53.2",
     "trl==0.20.0",
     "nbclient>=0.10.1",
     "pytest>=8.4.1",
@@ -91,6 +90,9 @@ select = ["I"]
 
 [tool.ruff.lint.isort]
 case-sensitive = false
+known-first-party = ["art"]
+known-third-party = ["wandb"]
+force-sort-within-sections = true
 
 [tool.pytest.ini_options]
 asyncio_mode = "auto"
diff --git a/scripts/bump_version.py b/scripts/bump_version.py
index f011abcaa..1f6000fdf 100755
--- a/scripts/bump_version.py
+++ b/scripts/bump_version.py
@@ -8,10 +8,10 @@
     python scripts/bump_version.py major  # 0.3.13 -> 1.0.0
 """
 
+from pathlib import Path
 import re
 import subprocess
 import sys
-from pathlib import Path
 
 
 def get_current_version():
diff --git a/scripts/migrate-s3-checkpoints.py b/scripts/migrate-s3-checkpoints.py
index 7348c63c2..01f8de9d3 100755
--- a/scripts/migrate-s3-checkpoints.py
+++ b/scripts/migrate-s3-checkpoints.py
@@ -13,8 +13,8 @@
 
 import argparse
 import asyncio
-import sys
 from pathlib import Path
+import sys
 
 # Add the src directory to the Python path
 sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
diff --git a/src/art/__init__.py b/src/art/__init__.py
index 787886b70..f977baa03 100644
--- a/src/art/__init__.py
+++ b/src/art/__init__.py
@@ -1,21 +1,30 @@
 import os
 
-# Import peft (and transformers by extension) before unsloth to enable sleep mode
-if os.environ.get("IMPORT_PEFT", "0") == "1":
-    import peft  # type: ignore # noqa: F401
+# Create a dummy GuidedDecodingParams class and inject it into vllm.sampling_params for trl compatibility
+try:
+    import vllm.sampling_params
+
+    class GuidedDecodingParams:
+        """Shim for vLLM 0.13+ where GuidedDecodingParams was removed."""
+
+        def __init__(self, **kwargs):
+            for key, value in kwargs.items():
+                setattr(self, key, value)
+
+    vllm.sampling_params.GuidedDecodingParams = GuidedDecodingParams  # type: ignore
+except ImportError:
+    pass  # vllm not installed
+
+# torch.cuda.MemPool doesn't currently support expandable_segments which is used in sleep mode
+conf = os.getenv("PYTORCH_CUDA_ALLOC_CONF", "").split(",")
+if "expandable_segments:True" in conf:
+    conf.remove("expandable_segments:True")
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = ",".join(conf)
 
 # Import unsloth before transformers, peft, and trl to maximize Unsloth optimizations
-# NOTE: If we import peft before unsloth to enable sleep mode, a warning will be shown
 if os.environ.get("IMPORT_UNSLOTH", "0") == "1":
     import unsloth  # type: ignore # noqa: F401
 
-if os.environ.get("IMPORT_PEFT", "0") == "1":
-    # torch.cuda.MemPool doesn't currently support expandable_segments which is used in sleep mode
-    conf = os.environ["PYTORCH_CUDA_ALLOC_CONF"].split(",")
-    if "expandable_segments:True" in conf:
-        conf.remove("expandable_segments:True")
-    os.environ["PYTORCH_CUDA_ALLOC_CONF"] = ",".join(conf)
-
 try:
     import transformers  # type: ignore
 
diff --git a/src/art/auto_trajectory.py b/src/art/auto_trajectory.py
index c1accafcd..4979602f7 100644
--- a/src/art/auto_trajectory.py
+++ b/src/art/auto_trajectory.py
@@ -1,16 +1,46 @@
 import contextvars
 import json
+import logging
 from typing import Any, AsyncIterator, Coroutine, Iterator, Literal, overload
 
 import httpx._models
-from openai import OpenAI
-from openai._streaming import Stream
-from openai.types.chat.chat_completion import Choice
+from openai.types.chat.chat_completion import ChatCompletion, Choice
 from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
 
-from .openai import consume_sync_chat_completion_stream
+from .openai import init_chat_completion, update_chat_completion
 from .trajectories import History, Trajectory
 
+logger = logging.getLogger(__name__)
+
+
+def parse_sse_to_chat_completion(content: bytes) -> ChatCompletion:
+    """Parse SSE (Server-Sent Events) content and build a ChatCompletion.
+
+    This handles the case where streaming responses have already been consumed
+    and we need to reconstruct the ChatCompletion from buffered bytes.
+    """
+    chat_completion: ChatCompletion | None = None
+
+    # Parse SSE format: each line starting with "data: " contains JSON
+    for line in content.decode("utf-8").split("\n"):
+        line = line.strip()
+        if not line.startswith("data: "):
+            continue
+        data = line[6:]  # Remove "data: " prefix
+        if data == "[DONE]":
+            continue
+
+        chunk_data = json.loads(data)
+        chunk = ChatCompletionChunk(**chunk_data)
+        if chat_completion is None:
+            chat_completion = init_chat_completion(chunk)
+        update_chat_completion(chat_completion, chunk)
+
+    if chat_completion is None:
+        raise ValueError("No valid chat completion chunks found in SSE content")
+
+    return chat_completion
+
 
 @overload
 def auto_trajectory(*, required: Literal[True]) -> Trajectory: ...
@@ -45,7 +75,6 @@ def __init__(self) -> None:
             messages_and_choices=[],
             reward=0.0,
         )
-        self.openai_client = OpenAI(api_key="")
 
     def __enter__(self) -> None:
         self.token = auto_trajectory_context_var.set(self)
@@ -54,47 +83,59 @@ def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
         auto_trajectory_context_var.reset(self.token)
 
     def handle_httpx_response(self, response: httpx._models.Response) -> None:
+        # Get buffered content (set by patched aiter_bytes/iter_bytes)
+        content = getattr(response, "_content_so_far", b"")
+        if not content:
+            # No content captured, nothing to process
+            return
+
         try:
             request_content = json.loads(getattr(response.request, "_content", b""))
-            messages = request_content["messages"]
-            tools = request_content.get("tools", None)
-            setattr(response, "_content", getattr(response, "_content_so_far", b""))
-            print(getattr(response, "_content"))
+        except (json.JSONDecodeError, AttributeError):
+            # Not a JSON request body, skip
+            return
+
+        messages = request_content.get("messages")
+        if messages is None:
+            # Not a chat completion request
+            return
+
+        tools = request_content.get("tools", None)
+
+        try:
             if request_content.get("stream", False):
-                choice = consume_sync_chat_completion_stream(
-                    Stream(
-                        cast_to=ChatCompletionChunk,
-                        response=response,
-                        client=self.openai_client,
-                    )
-                ).choices[0]
+                # Parse SSE content directly from buffered bytes
+                chat_completion = parse_sse_to_chat_completion(content)
+                choice = chat_completion.choices[0]
             else:
-                choice = Choice(
-                    **json.loads(getattr(response, "_content"))["choices"][0]
-                )
-            history: Trajectory | History = self.trajectory
-            history_index = -1
-            while True:
-                history_messages = history.messages()
-                if history_messages == messages[: len(history_messages)] and (
-                    history.tools == tools
-                    or (history_messages == [] and history.tools is None)
-                ):
-                    break
-                history_index += 1
-                try:
-                    history = self.trajectory.additional_histories[history_index]
-                except IndexError:
-                    history = History(messages_and_choices=[])
-                    self.trajectory.additional_histories.append(history)
-                    break
-            history.messages_and_choices.extend(
-                messages[len(history.messages_and_choices) :]
-            )
-            history.messages_and_choices.append(choice)
-            history.tools = tools
-        except:
-            pass
+                choice = Choice(**json.loads(content)["choices"][0])
+        except (json.JSONDecodeError, KeyError, ValueError) as e:
+            logger.debug(f"Failed to parse response content: {e}")
+            return
+
+        # Find the appropriate history to add this response to
+        history: Trajectory | History = self.trajectory
+        history_index = -1
+        while True:
+            history_messages = history.messages()
+            if history_messages == messages[: len(history_messages)] and (
+                history.tools == tools
+                or (history_messages == [] and history.tools is None)
+            ):
+                break
+            history_index += 1
+            try:
+                history = self.trajectory.additional_histories[history_index]
+            except IndexError:
+                history = History(messages_and_choices=[])
+                self.trajectory.additional_histories.append(history)
+                break
+
+        history.messages_and_choices.extend(
+            messages[len(history.messages_and_choices) :]
+        )
+        history.messages_and_choices.append(choice)
+        history.tools = tools
 
 
 auto_trajectory_context_var: contextvars.ContextVar[AutoTrajectoryContext] = (
diff --git a/src/art/backend.py b/src/art/backend.py
index f762a098a..6997cfa02 100644
--- a/src/art/backend.py
+++ b/src/art/backend.py
@@ -1,6 +1,6 @@
 import json
-import warnings
 from typing import TYPE_CHECKING, AsyncIterator, Literal
+import warnings
 
 import httpx
 from tqdm import auto as tqdm
diff --git a/src/art/cli.py b/src/art/cli.py
index 5b7fb6717..d6f17cf8c 100644
--- a/src/art/cli.py
+++ b/src/art/cli.py
@@ -1,14 +1,14 @@
 import json
-import socket
 from pathlib import Path
+import socket
 from typing import Any, AsyncIterator, Optional
 
-import pydantic
-import typer
-import uvicorn
 from dotenv import load_dotenv
 from fastapi import Body, FastAPI, Request
 from fastapi.responses import JSONResponse, StreamingResponse
+import pydantic
+import typer
+import uvicorn
 
 from . import dev
 from .errors import ARTError
diff --git a/src/art/dev/engine.py b/src/art/dev/engine.py
index d5dbf80fe..d0cef938c 100644
--- a/src/art/dev/engine.py
+++ b/src/art/dev/engine.py
@@ -125,4 +125,7 @@ class EngineArgs(TypedDict, total=False):
 
     additional_config: dict[str, Any] | None
 
-    disable_log_requests: bool
+    disable_log_requests: (
+        bool  # Deprecated in vLLM 0.13+, use enable_log_requests instead
+    )
+    enable_log_requests: bool  # New in vLLM 0.13+
diff --git a/src/art/dev/get_model_config.py b/src/art/dev/get_model_config.py
index 1b3a43de6..a5ae8692f 100644
--- a/src/art/dev/get_model_config.py
+++ b/src/art/dev/get_model_config.py
@@ -1,5 +1,3 @@
-import torch
-
 from .engine import EngineArgs
 from .model import InitArgs, InternalModelConfig, PeftArgs, TrainerArgs
 from .torchtune import TorchtuneArgs
@@ -14,30 +12,19 @@ def get_model_config(
 
     if config is None:
         config = InternalModelConfig()
+
     enable_sleep_mode = config.get("engine_args", {}).get("enable_sleep_mode", True)
     init_args = InitArgs(
-        disable_log_stats=False,
-        enable_prefix_caching=True,
-        fast_inference=True,
-        gpu_memory_utilization=(0.79 if enable_sleep_mode else 0.55),
+        fast_inference=False,
         load_in_4bit=True,
-        max_lora_rank=8,
         max_seq_length=32768,
         model_name=base_model,
-        use_async=True,
     )
-    if config.get("_decouple_vllm_and_unsloth", False):
-        init_args["fast_inference"] = False
-        init_args.pop("disable_log_stats")
-        init_args.pop("enable_prefix_caching")
-        init_args.pop("gpu_memory_utilization")
-        init_args.pop("max_lora_rank")
-        init_args.pop("use_async")
     engine_args = EngineArgs(
         allowed_local_media_path="/tmp",
-        disable_log_requests=True,
         enable_sleep_mode=enable_sleep_mode,
         generation_config="vllm",
+        model=base_model,
     )
     engine_args.update(config.get("engine_args", {}))
     init_args.update(config.get("init_args", {}))
@@ -47,8 +34,6 @@ def get_model_config(
             engine_args["model"] = last_checkpoint_dir
     elif config.get("torchtune_args") is not None:
         engine_args["model"] = base_model
-    if config.get("_decouple_vllm_and_unsloth", False):
-        engine_args["model"] = base_model
     peft_args = PeftArgs(
         lora_alpha=16,
         r=8,
@@ -94,5 +79,4 @@ def get_model_config(
         peft_args=peft_args,
         trainer_args=trainer_args,
         torchtune_args=torchtune_args,
-        _decouple_vllm_and_unsloth=config.get("_decouple_vllm_and_unsloth", False),
     )
diff --git a/src/art/dev/model.py b/src/art/dev/model.py
index 32626fae7..09bf8da5e 100644
--- a/src/art/dev/model.py
+++ b/src/art/dev/model.py
@@ -121,7 +121,6 @@ class InternalModelConfig(TypedDict, total=False):
     peft_args: "PeftArgs"
     trainer_args: "TrainerArgs"
     torchtune_args: TorchtuneArgs | None
-    _decouple_vllm_and_unsloth: bool
 
 
 class InitArgs(TypedDict, total=False):
diff --git a/src/art/dev/openai_server.py b/src/art/dev/openai_server.py
index f6639bda7..9e050e0a1 100644
--- a/src/art/dev/openai_server.py
+++ b/src/art/dev/openai_server.py
@@ -29,9 +29,7 @@ def get_openai_server_config(
     server_args.update(config.get("server_args", {}))
     engine_args = EngineArgs(
         model=base_model,
-        num_scheduler_steps=16 if lora_path else 1,
         served_model_name=base_model if lora_path else model_name,
-        disable_log_requests=True,
         generation_config="vllm",
     )
     engine_args.update(config.get("engine_args", {}))
diff --git a/src/art/gather.py b/src/art/gather.py
index a9a37624c..3461107c2 100644
--- a/src/art/gather.py
+++ b/src/art/gather.py
@@ -1,7 +1,7 @@
 import asyncio
+from collections import Counter
 import contextlib
 import contextvars
-from collections import Counter
 from dataclasses import dataclass, field
 from typing import Awaitable, Callable, Iterable, Iterator, Literal, overload
 
diff --git a/src/art/guided_completion.py b/src/art/guided_completion.py
index 4fdc10ea6..6599f486f 100644
--- a/src/art/guided_completion.py
+++ b/src/art/guided_completion.py
@@ -1,8 +1,11 @@
-import json
 from copy import deepcopy
+import json
 from typing import Iterable, List, Literal, Tuple
 
 from openai.types.chat.chat_completion import ChatCompletion
+from openai.types.chat.chat_completion_message_function_tool_call import (
+    ChatCompletionMessageFunctionToolCall,
+)
 from openai.types.chat.chat_completion_tool_choice_option_param import (
     ChatCompletionToolChoiceOptionParam,
 )
@@ -58,6 +61,9 @@ def get_guided_completion_params(
             raise ValueError("No tool call found in completion")
         if base_tools is None:
             raise ValueError("No base tools provided")
+        assert isinstance(tool_call, ChatCompletionMessageFunctionToolCall), (
+            "Only function tool calls are supported"
+        )
         tool_name = tool_call.function.name
         tool_choice = {
             "type": "function",  # ← must call it
diff --git a/src/art/langgraph/llm_wrapper.py b/src/art/langgraph/llm_wrapper.py
index 21d1889f9..11f2bbee6 100644
--- a/src/art/langgraph/llm_wrapper.py
+++ b/src/art/langgraph/llm_wrapper.py
@@ -4,8 +4,8 @@
 import contextvars
 import json
 import os
-import uuid
 from typing import Any, Literal
+import uuid
 
 from langchain_core.messages import HumanMessage, ToolMessage
 from langchain_core.prompt_values import ChatPromptValue
diff --git a/src/art/local/backend.py b/src/art/local/backend.py
index e99425c86..06fa17c08 100644
--- a/src/art/local/backend.py
+++ b/src/art/local/backend.py
@@ -1,34 +1,28 @@
 import asyncio
+from datetime import datetime
 import json
 import math
 import os
 import subprocess
-import warnings
-from datetime import datetime
 from types import TracebackType
 from typing import AsyncIterator, Literal, cast
+import warnings
 
 import aiohttp
 import numpy as np
+from openai import AsyncOpenAI
 import polars as pl
 import torch
-import weave
-from openai import AsyncOpenAI
 from tqdm import auto as tqdm
 from transformers import AutoImageProcessor, AutoTokenizer
 from transformers.image_processing_utils import BaseImageProcessor
 from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 from typing_extensions import Self
+import wandb
+from wandb.sdk.wandb_run import Run
+import weave
 from weave.trace.weave_client import WeaveClient
 
-import wandb
-from art.utils.deployment import (
-    DeploymentResult,
-    Provider,
-    TogetherDeploymentConfig,
-    WandbDeploymentConfig,
-    deploy_model,
-)
 from art.utils.old_benchmarking.calculate_step_metrics import calculate_step_std_dev
 from art.utils.output_dirs import (
     get_default_art_path,
@@ -44,7 +38,6 @@
 )
 from art.utils.trajectory_logging import write_trajectory_groups_parquet
 from mp_actors import close_proxy, move_to_child_process
-from wandb.sdk.wandb_run import Run
 
 from .. import dev
 from ..backend import Backend
@@ -137,7 +130,6 @@ async def register(
     async def _get_service(self, model: TrainableModel) -> ModelService:
         from ..dev.get_model_config import get_model_config
         from ..torchtune.service import TorchtuneService
-        from ..unsloth.decoupled_service import DecoupledUnslothService
         from ..unsloth.service import UnslothService
 
         if model.name not in self._services:
@@ -148,8 +140,6 @@ async def _get_service(self, model: TrainableModel) -> ModelService:
             )
             if config.get("torchtune_args") is not None:
                 service_class = TorchtuneService
-            elif config.get("_decouple_vllm_and_unsloth", False):
-                service_class = DecoupledUnslothService
             else:
                 service_class = UnslothService
             self._services[model.name] = service_class(
@@ -161,17 +151,9 @@ async def _get_service(self, model: TrainableModel) -> ModelService:
             if not self._in_process:
                 # Kill all "model-service" processes to free up GPU memory
                 subprocess.run(["pkill", "-9", "model-service"])
-                if isinstance(
-                    self._services[model.name],
-                    (UnslothService, DecoupledUnslothService),
-                ):
-                    # To enable sleep mode, import peft before unsloth
-                    # Unsloth will issue warnings, but everything appears to be okay
-                    if config.get("engine_args", {}).get("enable_sleep_mode", False):
-                        os.environ["IMPORT_PEFT"] = "1"
-                    # When moving the service to a child process, import unsloth
-                    # early to maximize optimizations
-                    os.environ["IMPORT_UNSLOTH"] = "1"
+                # When moving the service to a child process, import unsloth
+                # early to maximize optimizations
+                os.environ["IMPORT_UNSLOTH"] = "1"
                 self._services[model.name] = move_to_child_process(
                     self._services[model.name],
                     process_name="model-service",
@@ -309,43 +291,65 @@ async def _monitor_openai_server(
             base_url=base_url,
             api_key=api_key,
         )
+        consecutive_failures = 0
+        max_consecutive_failures = 3
         async with aiohttp.ClientSession() as session:
             while True:
                 # Wait 30 seconds before checking again
                 await asyncio.sleep(30)
-                # If the server is sleeping, skip the check
-                if await self._services[model_name].vllm_engine_is_sleeping():
-                    continue
-                # Check the metrics
-                async with session.get(
-                    f"{base_url.split('/v1')[0]}/metrics"
-                ) as response:
-                    metrics = await response.text()
-                # Parse Prometheus metrics for running requests
-                running_requests = 0
-                pending_requests = 0
-                for line in metrics.split("\n"):
-                    if line.startswith("vllm:num_requests_running"):
-                        running_requests = int(float(line.split()[1]))
-                    elif line.startswith("vllm:num_requests_waiting"):
-                        pending_requests = int(float(line.split()[1]))
-                # If there are no running or pending requests, send a health check
-                if running_requests == 0 and pending_requests == 0:
+                try:
+                    # If the server is sleeping, skip the check
+                    if await self._services[model_name].vllm_engine_is_sleeping():
+                        consecutive_failures = 0
+                        continue
+                    # Check the metrics with a timeout
+                    async with session.get(
+                        f"{base_url.split('/v1')[0]}/metrics",
+                        timeout=aiohttp.ClientTimeout(total=10),
+                    ) as response:
+                        metrics = await response.text()
+                    # Parse Prometheus metrics for running requests
+                    running_requests = 0
+                    pending_requests = 0
+                    for line in metrics.split("\n"):
+                        if line.startswith("vllm:num_requests_running"):
+                            running_requests = int(float(line.split()[1]))
+                        elif line.startswith("vllm:num_requests_waiting"):
+                            pending_requests = int(float(line.split()[1]))
+                    # If there are no running or pending requests, send a health check
+                    if running_requests == 0 and pending_requests == 0:
+                        try:
+                            # Send a health check with a short timeout
+                            await openai_client.completions.create(
+                                model=model_name,
+                                prompt="Hi",
+                                max_tokens=1,
+                                timeout=float(
+                                    os.environ.get("ART_SERVER_MONITOR_TIMEOUT", 5.0)
+                                ),
+                            )
+                        except Exception as e:
+                            # If the server is sleeping, a failed health check is okay
+                            if await self._services[
+                                model_name
+                            ].vllm_engine_is_sleeping():
+                                consecutive_failures = 0
+                                continue
+                            raise e
+                    # Reset failure counter on success
+                    consecutive_failures = 0
+                except Exception:
+                    # If the server is sleeping during an exception, it's okay
                     try:
-                        # Send a health check with a short timeout
-                        await openai_client.completions.create(
-                            model=model_name,
-                            prompt="Hi",
-                            max_tokens=1,
-                            timeout=float(
-                                os.environ.get("ART_SERVER_MONITOR_TIMEOUT", 5.0)
-                            ),
-                        )
-                    except Exception as e:
-                        # If the server is sleeping, a failed health check is okay
                         if await self._services[model_name].vllm_engine_is_sleeping():
+                            consecutive_failures = 0
                             continue
-                        raise e
+                    except Exception:
+                        pass  # If we can't check sleeping status, count it as a failure
+                    consecutive_failures += 1
+                    if consecutive_failures >= max_consecutive_failures:
+                        raise
+                    # Otherwise, continue and try again
 
     async def _log(
         self,
diff --git a/src/art/loss.py b/src/art/loss.py
index 74fb68685..98bc7db15 100644
--- a/src/art/loss.py
+++ b/src/art/loss.py
@@ -1,7 +1,7 @@
 from typing import TYPE_CHECKING
 
-import torch
 from pydantic import BaseModel, ConfigDict
+import torch
 
 from art import dev
 from art.utils.group_aggregate import group_aggregate
diff --git a/src/art/mcp/types.py b/src/art/mcp/types.py
index 9adbddc19..ea88ed216 100644
--- a/src/art/mcp/types.py
+++ b/src/art/mcp/types.py
@@ -1,7 +1,7 @@
-import json
-import random
 from collections import Counter
 from dataclasses import dataclass
+import json
+import random
 from typing import Any, Dict, List, Optional
 
 from art.utils.logging import _C, dim, info
diff --git a/src/art/openai.py b/src/art/openai.py
index a56fcabdf..8e70cdcf1 100644
--- a/src/art/openai.py
+++ b/src/art/openai.py
@@ -7,10 +7,10 @@
     ChatCompletionMessage,
     FunctionCall,
 )
-from openai.types.chat.chat_completion_message_tool_call import (
-    ChatCompletionMessageToolCall,
-    Function,
+from openai.types.chat.chat_completion_message_function_tool_call import (
+    ChatCompletionMessageFunctionToolCall,
 )
+from openai.types.chat.chat_completion_message_tool_call import Function
 
 
 async def consume_chat_completion_stream(
@@ -115,26 +115,30 @@ def update_chat_completion(
         if chunk_choice.delta.tool_calls:
             if choice.message.tool_calls is None:
                 choice.message.tool_calls = []
-            for tool_call in chunk_choice.delta.tool_calls:
-                while tool_call.index not in range(len(choice.message.tool_calls)):
+            for tool_call_delta in chunk_choice.delta.tool_calls:
+                while tool_call_delta.index not in range(
+                    len(choice.message.tool_calls)
+                ):
                     choice.message.tool_calls.append(
-                        ChatCompletionMessageToolCall(
+                        ChatCompletionMessageFunctionToolCall(
                             id="",
                             function=Function(arguments="", name=""),
                             type="function",
                         )
                     )
-                if tool_call.id:
-                    choice.message.tool_calls[tool_call.index].id = tool_call.id
-                if tool_call.function:
-                    if tool_call.function.name:
-                        choice.message.tool_calls[
-                            tool_call.index
-                        ].function.name = tool_call.function.name
-                    if tool_call.function.arguments:
-                        choice.message.tool_calls[
-                            tool_call.index
-                        ].function.arguments += tool_call.function.arguments
+                if tool_call_delta.id:
+                    choice.message.tool_calls[
+                        tool_call_delta.index
+                    ].id = tool_call_delta.id
+                if tool_call_delta.function:
+                    tool_call = choice.message.tool_calls[tool_call_delta.index]
+                    assert isinstance(tool_call, ChatCompletionMessageFunctionToolCall)
+                    if tool_call_delta.function.name:
+                        tool_call.function.name = tool_call_delta.function.name
+                    if tool_call_delta.function.arguments:
+                        tool_call.function.arguments += (
+                            tool_call_delta.function.arguments
+                        )
         if getattr(chunk_choice.delta, "reasoning", None):
             if not hasattr(choice.message, "reasoning"):
                 setattr(choice.message, "reasoning", "")
diff --git a/src/art/preprocessing/tokenize.py b/src/art/preprocessing/tokenize.py
index 6ffc462f4..a9807b052 100644
--- a/src/art/preprocessing/tokenize.py
+++ b/src/art/preprocessing/tokenize.py
@@ -1,11 +1,11 @@
-import math
-import random
 from dataclasses import dataclass
 from itertools import takewhile
+import math
+import random
 from typing import Any, Generator, cast
 
-import torch
 from PIL import Image
+import torch
 from transformers.image_processing_utils import BaseImageProcessor
 from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 
diff --git a/src/art/serverless/backend.py b/src/art/serverless/backend.py
index fc8268d47..2d5de8490 100644
--- a/src/art/serverless/backend.py
+++ b/src/art/serverless/backend.py
@@ -1,6 +1,6 @@
 import asyncio
-import warnings
 from typing import TYPE_CHECKING, AsyncIterator, Literal
+import warnings
 
 from openai._types import NOT_GIVEN
 from tqdm import auto as tqdm
diff --git a/src/art/skypilot/backend.py b/src/art/skypilot/backend.py
index 68bdfb785..5844b8cc4 100644
--- a/src/art/skypilot/backend.py
+++ b/src/art/skypilot/backend.py
@@ -1,12 +1,12 @@
 import asyncio
-import os
 from importlib.metadata import PackageNotFoundError, version
+import os
 from typing import TYPE_CHECKING, Literal, cast
 
+from dotenv import dotenv_values
 import semver
 import sky
 import sky.clouds
-from dotenv import dotenv_values
 
 from .. import dev
 from ..backend import Backend
diff --git a/src/art/torchtune/recipe.py b/src/art/torchtune/recipe.py
index b3f83b506..581bad5c4 100644
--- a/src/art/torchtune/recipe.py
+++ b/src/art/torchtune/recipe.py
@@ -7,17 +7,17 @@
 # Modifications:
 # Bradley Hilton, OpenPipe Inc., and other ART contributors.
 
+from functools import partial
 import os
 import sys
 import time
-from functools import partial
 from typing import Any, Optional, Union, cast
 from warnings import warn
 
-import setproctitle
-import torch
 from omegaconf import DictConfig, OmegaConf
 from pydantic import ValidationError
+import setproctitle
+import torch
 from torch.distributed import destroy_process_group, init_process_group
 from torch.distributed.device_mesh import DeviceMesh
 from torch.distributed.fsdp import FSDPModule
@@ -1067,8 +1067,8 @@ def train(self, *args: Any, **kwargs: Any) -> None:
 
     def _get_micro_batches(self, curr_epoch: int) -> tuple[list[PackedTensors], Batch]:
         import math
-        import time
         from pathlib import Path
+        import time
 
         from safetensors.torch import save_file
 
diff --git a/src/art/torchtune/service.py b/src/art/torchtune/service.py
index 1c0005168..2fbdff8cb 100644
--- a/src/art/torchtune/service.py
+++ b/src/art/torchtune/service.py
@@ -1,17 +1,17 @@
 import asyncio
-import glob
-import logging
-import os
-import time
 from collections import Counter
 from dataclasses import dataclass
 from functools import cached_property
+import glob
+import logging
+import os
 from pathlib import Path
+import time
 from typing import AsyncIterator
 
+from safetensors.torch import load_file
 import torch
 import torchtune
-from safetensors.torch import load_file
 from vllm import AsyncEngineArgs
 from vllm.v1.engine.async_llm import AsyncLLM
 
diff --git a/src/art/trajectories.py b/src/art/trajectories.py
index 5ff48c94c..69ab17db9 100644
--- a/src/art/trajectories.py
+++ b/src/art/trajectories.py
@@ -1,8 +1,8 @@
 import asyncio
-import time
-import traceback
 from contextlib import asynccontextmanager
 from datetime import datetime
+import time
+import traceback
 from typing import (
     Any,
     AsyncGenerator,
@@ -13,8 +13,8 @@
     overload,
 )
 
-import pydantic
 from openai.types.chat.chat_completion import Choice
+import pydantic
 
 from .types import Messages, MessagesAndChoices, Tools
 
@@ -106,14 +106,7 @@ def get_messages(messages_and_choices: MessagesAndChoices) -> Messages:
                     **(
                         {
                             "tool_calls": [
-                                {
-                                    "id": tool_call.id,
-                                    "type": tool_call.type,
-                                    "function": {
-                                        "name": tool_call.function.name,
-                                        "arguments": tool_call.function.arguments,
-                                    },
-                                }
+                                tool_call.model_dump(mode="json")
                                 for tool_call in tool_calls
                             ]
                         }
diff --git a/src/art/types.py b/src/art/types.py
index 85ec6a7de..c3a82d049 100644
--- a/src/art/types.py
+++ b/src/art/types.py
@@ -1,9 +1,9 @@
 from typing import Annotated, Literal
 
-import pydantic
 from openai.types.chat.chat_completion import Choice
 from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
 from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam
+import pydantic
 from pydantic import SkipValidation
 
 Message = Annotated[ChatCompletionMessageParam, SkipValidation]
diff --git a/src/art/unsloth/decoupled_service.py b/src/art/unsloth/decoupled_service.py
deleted file mode 100644
index 5ea3d082a..000000000
--- a/src/art/unsloth/decoupled_service.py
+++ /dev/null
@@ -1,361 +0,0 @@
-import asyncio
-import logging
-import os
-import time
-from collections import Counter
-from dataclasses import dataclass
-from functools import cached_property
-from typing import Any, AsyncIterator, cast
-
-import peft
-import torch
-from datasets import Dataset
-from transformers.tokenization_utils_base import PreTrainedTokenizerBase
-from transformers.utils.dummy_pt_objects import GenerationMixin, PreTrainedModel
-from trl import GRPOConfig, GRPOTrainer
-from vllm import AsyncEngineArgs
-from vllm.device_allocator.cumem import CuMemAllocator
-from vllm.lora.request import LoRARequest
-from vllm.v1.engine.async_llm import AsyncLLM
-from vllm.v1.worker.gpu_worker import logger
-
-from .. import dev, types
-from ..local.checkpoints import get_last_checkpoint_dir
-from ..preprocessing.pack import (
-    DiskPackedTensors,
-    PackedTensors,
-    packed_tensors_from_dir,
-)
-from ..utils.get_model_step import get_step_from_dir
-from ..utils.output_dirs import get_step_checkpoint_dir
-from ..vllm import get_llm, get_worker, openai_server_task, run_on_workers
-from .train import gc_and_empty_cuda_cache, train
-
-
-class CausalLM(PreTrainedModel, GenerationMixin):
-    """Dummy class for type checking."""
-
-    pass
-
-
-class TrainInputs(PackedTensors):
-    config: types.TrainConfig
-    _config: dev.TrainConfig
-    return_new_logprobs: bool
-
-
-@dataclass
-class UnslothState:
-    model: CausalLM
-    tokenizer: PreTrainedTokenizerBase
-    peft_model: peft.peft_model.PeftModelForCausalLM
-    trainer: GRPOTrainer
-    inputs_queue: asyncio.Queue[TrainInputs]
-    results_queue: asyncio.Queue[dict[str, float]]
-
-
-@dataclass
-class DecoupledUnslothService:
-    model_name: str
-    base_model: str
-    config: dev.InternalModelConfig
-    output_dir: str
-    _is_sleeping: bool = False
-
-    async def start_openai_server(self, config: dev.OpenAIServerConfig | None) -> None:
-        lora_path = get_last_checkpoint_dir(self.output_dir)
-        if lora_path is None:
-            # Create initial LoRA checkpoint if none exists
-            lora_path = get_step_checkpoint_dir(self.output_dir, 0)
-            os.makedirs(os.path.dirname(lora_path), exist_ok=True)
-            self._state.trainer.save_model(lora_path)
-        await openai_server_task(
-            engine=await self.llm,
-            config=dev.get_openai_server_config(
-                model_name=self.model_name,
-                base_model=self.base_model,
-                log_file=f"{self.output_dir}/logs/vllm.log",
-                lora_path=lora_path,
-                config=config,
-            ),
-        )
-
-    async def vllm_engine_is_sleeping(self) -> bool:
-        return self._is_sleeping
-
-    async def train(
-        self,
-        disk_packed_tensors: DiskPackedTensors,
-        config: types.TrainConfig,
-        _config: dev.TrainConfig,
-        verbose: bool = False,
-    ) -> AsyncIterator[dict[str, float]]:
-        llm = await self.llm
-        pids_path = f"{self.output_dir}/pids.txt"
-        # reset the pids file
-        with open(pids_path, "w") as f:
-            f.write("")
-        # start putting the workers to sleep
-        sleep_task = asyncio.create_task(
-            run_on_workers(
-                llm,
-                sleep,
-                level=1,
-                pids_path=pids_path,
-                profile=verbose,
-            )
-        )
-        # wait for the workers to write their pids twice, indicating that they are asleep
-        while True:
-            pids = Counter(open(pids_path).read().splitlines())
-            if set(pids.values()) == {2}:
-                break
-            await asyncio.sleep(0.25)
-
-        self._is_sleeping = True
-
-        # Free memory after vLLM workers are asleep
-        gc_and_empty_cuda_cache()
-
-        # Load packed tensors
-        packed_tensors = packed_tensors_from_dir(**disk_packed_tensors)
-
-        # Wait for existing batches to finish
-        await self._state.results_queue.join()
-
-        # If we haven't already, start the training task
-        if not hasattr(self, "_train_task") or self._train_task is None:
-            self._train_task = asyncio.create_task(
-                train(
-                    trainer=self._state.trainer,
-                    results_queue=self._state.results_queue,
-                )
-            )
-            warmup = True
-        else:
-            warmup = False
-        precalculate_logprobs = _config.get("precalculate_logprobs", False)
-        # Train on the batch
-        for offset in range(0, packed_tensors["tokens"].shape[0]):
-            for _ in range(2 if warmup else 1):
-                if precalculate_logprobs and not warmup:
-                    packed_tensors["logprobs"] = torch.cat(
-                        [
-                            self._state.trainer.compute_loss(
-                                self._state.peft_model,
-                                TrainInputs(
-                                    **{
-                                        k: v[_offset : _offset + 1]
-                                        for k, v in packed_tensors.items()
-                                        if isinstance(v, torch.Tensor)
-                                    },
-                                    pixel_values=packed_tensors["pixel_values"][
-                                        _offset : _offset + 1
-                                    ],
-                                    image_grid_thw=packed_tensors["image_grid_thw"][
-                                        _offset : _offset + 1
-                                    ],
-                                    config=config,
-                                    _config=_config,
-                                    return_new_logprobs=True,
-                                ),  # type: ignore
-                            )
-                            for _offset in range(0, packed_tensors["tokens"].shape[0])
-                        ]
-                    ).to("cpu")
-                    precalculate_logprobs = False
-                self._state.inputs_queue.put_nowait(
-                    TrainInputs(
-                        **{
-                            k: (
-                                v[offset : offset + 1, :1024]
-                                if warmup and v.dim() > 1
-                                else v[offset : offset + 1]
-                            )
-                            for k, v in packed_tensors.items()
-                            if isinstance(v, torch.Tensor)
-                        },
-                        pixel_values=(
-                            [None]
-                            if warmup
-                            else packed_tensors["pixel_values"][offset : offset + 1]
-                        ),
-                        image_grid_thw=(
-                            [None]
-                            if warmup
-                            else packed_tensors["image_grid_thw"][offset : offset + 1]
-                        ),
-                        config=(
-                            config.model_copy(
-                                update={"lr": 1e-9, "beta": 0.0, "kl_coef": 0.0}
-                            )
-                            if warmup
-                            else config
-                        ),
-                        _config=_config,
-                        return_new_logprobs=False,
-                    )
-                )
-                # Wait for a result from the queue or for the training task to,
-                # presumably, raise an exception
-                done, _ = await asyncio.wait(
-                    [
-                        asyncio.create_task(self._state.results_queue.get()),
-                        self._train_task,
-                    ],
-                    return_when=asyncio.FIRST_COMPLETED,
-                )
-                if verbose:
-                    print(
-                        "Done waiting for a result from the queue or for the training task to, presumably, raise an exception"
-                    )
-                for task in done:
-                    result = task.result()
-                    # If `result` is `None`, the training task finished somehow.
-                    assert result is not None, "The training task should never finish."
-                    self._state.results_queue.task_done()
-                    if warmup:
-                        gc_and_empty_cuda_cache()
-                        await asyncio.sleep(0.1)
-                        warmup = False
-                    else:
-                        yield result
-
-        if verbose:
-            print("Saving new LoRA adapter...")
-        # Save checkpoint after training
-        next_step = get_step_from_dir(self.output_dir) + 1
-        checkpoint_dir = get_step_checkpoint_dir(self.output_dir, next_step)
-        os.makedirs(checkpoint_dir, exist_ok=True)
-        self._state.trainer.save_model(checkpoint_dir)
-
-        # Free memory before waking up vLLM
-        gc_and_empty_cuda_cache()
-
-        # Remove pids.txt to signal workers to wake up
-        if os.path.exists(pids_path):
-            os.remove(pids_path)
-            if verbose:
-                print("Removed pids.txt to signal workers to wake up")
-
-        # wait for the workers to wake up
-        await sleep_task
-
-        self._is_sleeping = False
-
-        # swap out the LoRA adapter
-        await llm.remove_lora(1)
-        await llm.add_lora(
-            LoRARequest(
-                lora_name=self.model_name,
-                lora_int_id=1,
-                lora_path=checkpoint_dir,
-            )
-        )
-
-        if verbose:
-            print("DecoupledUnslothService.train complete")
-
-    @cached_property
-    def _state(self) -> UnslothState:
-        import unsloth
-
-        # Initialize Unsloth model
-        init_args = self.config.get("init_args", {})
-        checkpoint_dir = get_last_checkpoint_dir(self.output_dir)
-        if checkpoint_dir:
-            init_args["model_name"] = checkpoint_dir
-        else:
-            init_args["model_name"] = self.base_model
-
-        model, tokenizer = cast(
-            tuple[CausalLM, PreTrainedTokenizerBase],
-            unsloth.FastLanguageModel.from_pretrained(**init_args),
-        )
-
-        # Initialize PEFT model
-        peft_model = cast(
-            peft.peft_model.PeftModelForCausalLM,
-            unsloth.FastLanguageModel.get_peft_model(
-                model, **self.config.get("peft_args", {})
-            ),
-        )
-
-        # Initialize trainer with dummy dataset
-        data = {"prompt": ""}
-        trainer = GRPOTrainer(
-            model=peft_model,  # type: ignore
-            reward_funcs=[],
-            args=GRPOConfig(**self.config.get("trainer_args", {})),  # type: ignore
-            train_dataset=Dataset.from_list([data for _ in range(10_000_000)]),
-            processing_class=tokenizer,
-        )
-
-        # Initialize queues
-        inputs_queue: asyncio.Queue[TrainInputs] = asyncio.Queue()
-        results_queue: asyncio.Queue[dict[str, float]] = asyncio.Queue()
-
-        # Patch trainer _prepare_inputs() to pull from queue
-        def _async_prepare_inputs(*_: Any, **__: Any) -> dict[str, torch.Tensor]:
-            async def get_inputs() -> TrainInputs:
-                return await inputs_queue.get()
-
-            # Force otherwise synchronous _prepare_inputs() to yield
-            # with nested asyncio.run() call
-            inputs = asyncio.run(get_inputs())
-
-            return cast(dict[str, torch.Tensor], inputs)
-
-        trainer._prepare_inputs = _async_prepare_inputs
-
-        return UnslothState(
-            model=model,
-            tokenizer=tokenizer,
-            peft_model=peft_model,
-            trainer=trainer,
-            inputs_queue=inputs_queue,
-            results_queue=results_queue,
-        )
-
-    @cached_property
-    def llm(self) -> asyncio.Task[AsyncLLM]:
-        return asyncio.create_task(
-            get_llm(
-                AsyncEngineArgs(
-                    **{**self.config.get("engine_args", {}), "enable_lora": True}
-                )
-            )
-        )
-
-
-def sleep(*, level: int, pids_path: str, profile: bool) -> None:
-    """
-    Put the worker to sleep until signaled to wake up.
-
-    Args:
-        level: The sleep level: 1 to offload the kv cache, 2 to discard the kv cache.
-        pids_path: The path to the file that contains the PIDs of the workers.
-        profile: Whether to profile
-    """
-    with open(pids_path, "a") as f:
-        f.write(f"{os.getpid()}\n")
-    worker = get_worker()
-    allocator = CuMemAllocator.get_instance()
-    try:
-        if not (profile and worker.rank == 0):
-            logger.setLevel(logging.CRITICAL)
-        setattr(allocator, "_override_tags", {"weights", "kv_cache"})
-        with worker.time("sleep"):
-            worker.sleep(level)
-        with open(pids_path, "a") as f:
-            f.write(f"{os.getpid()}\n")
-
-        # Wait for the signal to wake up
-        while os.path.exists(pids_path):
-            time.sleep(1)
-
-        with worker.time("wake_up"):
-            worker.wake_up()
-    finally:
-        logger.setLevel(logging.INFO)
-        delattr(allocator, "_override_tags")
diff --git a/src/art/unsloth/service.py b/src/art/unsloth/service.py
index 621bc9b0e..e025d3235 100644
--- a/src/art/unsloth/service.py
+++ b/src/art/unsloth/service.py
@@ -1,12 +1,20 @@
+"""Unsloth training service with decoupled vLLM inference."""
+
 import asyncio
-import functools
-import os
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, AsyncIterator
+from functools import cached_property
+import os
+from typing import TYPE_CHECKING, Any, AsyncIterator, Protocol, cast
 
+from datasets import Dataset
+import peft
 import torch
-
-from art.utils.get_model_step import get_step_from_dir
+from transformers.tokenization_utils_base import PreTrainedTokenizerBase
+from transformers.utils.dummy_pt_objects import GenerationMixin, PreTrainedModel
+from trl import GRPOConfig, GRPOTrainer
+from vllm import AsyncEngineArgs
+from vllm.lora.request import LoRARequest
+from vllm.v1.engine.async_llm import AsyncLLM
 
 from .. import dev, types
 from ..local.checkpoints import get_last_checkpoint_dir
@@ -15,52 +23,299 @@
     PackedTensors,
     packed_tensors_from_dir,
 )
-from .train import train
+from ..utils.get_model_step import get_step_from_dir
+from ..utils.output_dirs import get_step_checkpoint_dir
+from ..vllm import get_llm, get_worker, openai_server_task, run_on_workers
+from .train import gc_and_empty_cuda_cache, train
 
 if TYPE_CHECKING:
-    from unsloth_zoo.vllm_lora_request import LoRARequest  # type: ignore
+    from peft.peft_model import PeftModelForCausalLM
+    from trl import GRPOTrainer
+
+
+# ============================================================================
+# Shared Utilities
+# ============================================================================
+
+
+class SupportsLoadLora(Protocol):
+    """Protocol for models that support the optimized load_lora method."""
 
-    from .state import ModelState
+    def load_lora(self, lora_path: str, load_tensors: bool = True) -> LoRARequest: ...
 
 
 class TrainInputs(PackedTensors):
+    """Training inputs with config attached."""
+
     config: types.TrainConfig
     _config: dev.TrainConfig
     return_new_logprobs: bool
 
 
+def create_train_inputs(
+    packed_tensors: PackedTensors,
+    offset: int,
+    config: types.TrainConfig,
+    _config: dev.TrainConfig,
+    warmup: bool,
+) -> TrainInputs:
+    """Create TrainInputs for a single batch offset."""
+    return TrainInputs(
+        **{
+            k: (
+                v[offset : offset + 1, :1024]
+                if warmup and v.dim() > 1
+                else v[offset : offset + 1]
+            )
+            for k, v in packed_tensors.items()
+            if isinstance(v, torch.Tensor)
+        },
+        pixel_values=(
+            [None] if warmup else packed_tensors["pixel_values"][offset : offset + 1]
+        ),
+        image_grid_thw=(
+            [None] if warmup else packed_tensors["image_grid_thw"][offset : offset + 1]
+        ),
+        config=(
+            config.model_copy(update={"lr": 1e-9, "beta": 0.0, "kl_coef": 0.0})
+            if warmup
+            else config
+        ),
+        _config=_config,
+        return_new_logprobs=False,
+    )
+
+
+def precalculate_new_logprobs(
+    trainer: "GRPOTrainer",
+    peft_model: "PeftModelForCausalLM",
+    packed_tensors: PackedTensors,
+    config: types.TrainConfig,
+    _config: dev.TrainConfig,
+) -> torch.Tensor:
+    """Precalculate logprobs for all offsets and return as a tensor."""
+    return torch.cat(
+        [
+            trainer.compute_loss(
+                peft_model,
+                TrainInputs(
+                    **{
+                        k: v[_offset : _offset + 1]
+                        for k, v in packed_tensors.items()
+                        if isinstance(v, torch.Tensor)
+                    },
+                    pixel_values=packed_tensors["pixel_values"][_offset : _offset + 1],
+                    image_grid_thw=packed_tensors["image_grid_thw"][
+                        _offset : _offset + 1
+                    ],
+                    config=config,
+                    _config=_config,
+                    return_new_logprobs=True,
+                ),  # type: ignore
+            )
+            for _offset in range(0, packed_tensors["tokens"].shape[0])
+        ]
+    ).to("cpu")
+
+
+async def process_train_batch(
+    packed_tensors: PackedTensors,
+    config: types.TrainConfig,
+    _config: dev.TrainConfig,
+    inputs_queue: asyncio.Queue[TrainInputs],
+    results_queue: asyncio.Queue[dict[str, float]],
+    train_task: asyncio.Task[None],
+    trainer: "GRPOTrainer",
+    peft_model: "PeftModelForCausalLM",
+    warmup: bool,
+    verbose: bool = False,
+):
+    """
+    Process training batches and yield results.
+
+    Yields tuples of (result, warmup_done) where warmup_done indicates if warmup just finished.
+    """
+    precalculate_logprobs = _config.get("precalculate_logprobs", False)
+
+    for offset in range(0, packed_tensors["tokens"].shape[0]):
+        for _ in range(2 if warmup else 1):
+            if precalculate_logprobs and not warmup:
+                # Preserve original logprobs before overwriting
+                packed_tensors["original_logprobs"] = packed_tensors["logprobs"]  # type: ignore
+                packed_tensors["logprobs"] = precalculate_new_logprobs(
+                    trainer, peft_model, packed_tensors, config, _config
+                )
+                precalculate_logprobs = False
+
+            inputs_queue.put_nowait(
+                create_train_inputs(packed_tensors, offset, config, _config, warmup)
+            )
+
+            # Wait for a result from the queue or for the training task to,
+            # presumably, raise an exception
+            done, _ = await asyncio.wait(
+                [
+                    asyncio.create_task(results_queue.get()),
+                    train_task,
+                ],
+                return_when=asyncio.FIRST_COMPLETED,
+            )
+            if verbose:
+                print(
+                    "Done waiting for a result from the queue or for the training task to, presumably, raise an exception"
+                )
+            for task in done:
+                result = task.result()
+                # If `result` is `None`, the training task finished somehow.
+                assert result is not None, "The training task should never finish."
+                results_queue.task_done()
+                if warmup:
+                    gc_and_empty_cuda_cache()
+                    await asyncio.sleep(0.1)
+                    warmup = False
+                else:
+                    yield result
+
+
+def save_checkpoint(
+    trainer: "GRPOTrainer",
+    output_dir: str,
+    verbose: bool = False,
+) -> str:
+    """Save a checkpoint and return the checkpoint directory path."""
+    if verbose:
+        print("Saving new LoRA adapter...")
+    next_step = get_step_from_dir(output_dir) + 1
+    checkpoint_dir = get_step_checkpoint_dir(output_dir, next_step)
+    os.makedirs(checkpoint_dir, exist_ok=True)
+    trainer.save_model(checkpoint_dir)
+    return checkpoint_dir
+
+
+# ============================================================================
+# Model Classes
+# ============================================================================
+
+
+class CausalLM(PreTrainedModel, GenerationMixin):
+    """Dummy class for type checking."""
+
+    pass
+
+
+@dataclass
+class UnslothState:
+    model: CausalLM
+    tokenizer: PreTrainedTokenizerBase
+    peft_model: peft.peft_model.PeftModelForCausalLM
+    trainer: GRPOTrainer
+    inputs_queue: asyncio.Queue[TrainInputs]
+    results_queue: asyncio.Queue[dict[str, float]]
+    _is_offloaded: bool = False
+    _pinned_buffers: dict[str, torch.Tensor] | None = None
+
+    def offload_to_cpu(self) -> None:
+        """Offload training model and optimizer to CPU using pinned memory for faster transfers."""
+        if self._is_offloaded:
+            return
+
+        # Initialize pinned buffer storage
+        if self._pinned_buffers is None:
+            self._pinned_buffers = {}
+
+        # Offload model parameters to pinned memory for faster reload
+        for name, param in self.peft_model.named_parameters():
+            if param.device.type == "cuda":
+                # Create pinned buffer if not exists or wrong size
+                if (
+                    name not in self._pinned_buffers
+                    or self._pinned_buffers[name].shape != param.shape
+                ):
+                    self._pinned_buffers[name] = torch.empty(
+                        param.shape, dtype=param.dtype, device="cpu", pin_memory=True
+                    )
+                # Async copy to pinned memory
+                self._pinned_buffers[name].copy_(param.data, non_blocking=True)
+                param.data = self._pinned_buffers[name]
+
+        # Offload optimizer state to pinned memory
+        optimizer = getattr(self.trainer, "optimizer", None)
+        if optimizer is not None and hasattr(optimizer, "state"):
+            for param_id, state in optimizer.state.items():
+                for k, v in state.items():
+                    if isinstance(v, torch.Tensor) and v.device.type == "cuda":
+                        key = f"opt_{id(param_id)}_{k}"
+                        if (
+                            key not in self._pinned_buffers
+                            or self._pinned_buffers[key].shape != v.shape
+                        ):
+                            self._pinned_buffers[key] = torch.empty(
+                                v.shape, dtype=v.dtype, device="cpu", pin_memory=True
+                            )
+                        self._pinned_buffers[key].copy_(v, non_blocking=True)
+                        state[k] = self._pinned_buffers[key]
+
+        # Sync to ensure all copies are complete before freeing GPU memory
+        torch.cuda.synchronize()
+
+        self._is_offloaded = True
+        gc_and_empty_cuda_cache()
+
+    def reload_to_gpu(self, device: str = "cuda:0") -> None:
+        """Reload training model and optimizer back to GPU using async transfers."""
+        if not self._is_offloaded:
+            return
+
+        # Reload model parameters from pinned memory (fast async transfer)
+        for name, param in self.peft_model.named_parameters():
+            if param.device.type == "cpu":
+                # Allocate on GPU and async copy from pinned memory
+                gpu_tensor = torch.empty(param.shape, dtype=param.dtype, device=device)
+                gpu_tensor.copy_(param.data, non_blocking=True)
+                param.data = gpu_tensor
+
+        # Reload optimizer state
+        optimizer = getattr(self.trainer, "optimizer", None)
+        if optimizer is not None and hasattr(optimizer, "state"):
+            for state in optimizer.state.values():
+                for k, v in state.items():
+                    if isinstance(v, torch.Tensor) and v.device.type == "cpu":
+                        gpu_tensor = torch.empty(v.shape, dtype=v.dtype, device=device)
+                        gpu_tensor.copy_(v, non_blocking=True)
+                        state[k] = gpu_tensor
+
+        # Sync to ensure all copies are complete before training
+        torch.cuda.synchronize()
+
+        self._is_offloaded = False
+
+
+# ============================================================================
+# Service
+# ============================================================================
+
+
 @dataclass
 class UnslothService:
     model_name: str
     base_model: str
     config: dev.InternalModelConfig
     output_dir: str
-    _openai_server_task: asyncio.Task[None] | None = None
-    _train_task: asyncio.Task[None] | None = None
-
-    @functools.cached_property
-    def state(self) -> "ModelState":
-        from .state import ModelState
-
-        return ModelState(self.config)
-
-    @functools.cached_property
-    def results_queue(self) -> asyncio.Queue[dict[str, float]]:
-        return asyncio.Queue()
+    _is_sleeping: bool = False
 
     async def start_openai_server(self, config: dev.OpenAIServerConfig | None) -> None:
-        from ..vllm import openai_server_task
-
         lora_path = get_last_checkpoint_dir(self.output_dir)
         if lora_path is None:
-            from ..utils.output_dirs import get_step_checkpoint_dir
-
+            # Create initial LoRA checkpoint if none exists
             lora_path = get_step_checkpoint_dir(self.output_dir, 0)
             os.makedirs(os.path.dirname(lora_path), exist_ok=True)
-            self.state.trainer.save_model(lora_path)
-        await self.stop_openai_server()
-        self._openai_server_task = await openai_server_task(
-            engine=self.state.vllm.async_engine,
+            self._state.trainer.save_model(lora_path)
+
+        # Offload training model to CPU before vLLM starts to free GPU memory
+        self._state.offload_to_cpu()
+
+        await openai_server_task(
+            engine=await self.llm,
             config=dev.get_openai_server_config(
                 model_name=self.model_name,
                 base_model=self.base_model,
@@ -69,15 +324,9 @@ async def start_openai_server(self, config: dev.OpenAIServerConfig | None) -> No
                 config=config,
             ),
         )
-        self._set_lora(lora_path)
 
     async def vllm_engine_is_sleeping(self) -> bool:
-        return await self.state.vllm.async_engine.is_sleeping()
-
-    async def stop_openai_server(self) -> None:
-        if self._openai_server_task:
-            self._openai_server_task.cancel()
-            self._openai_server_task = None
+        return self._is_sleeping
 
     async def train(
         self,
@@ -86,153 +335,287 @@ async def train(
         _config: dev.TrainConfig,
         verbose: bool = False,
     ) -> AsyncIterator[dict[str, float]]:
-        # Get the packed tensors from disk
+        llm = await self.llm
+
+        # Pause generation to prevent new requests during training
+        await llm.pause_generation()
+
+        # Determine sleep level based on outstanding requests:
+        # - level 1: offload KV cache to CPU (can resume with existing KV state)
+        # - level 2: discard KV cache (fresh start after wake)
+        has_unfinished = llm.output_processor.has_unfinished_requests()
+        if has_unfinished:
+            sleep_level = 1
+        else:
+            # Reset prefix cache before discarding KV cache
+            await llm.reset_prefix_cache()
+            sleep_level = 2
+
+        # Put workers to sleep
+        await run_on_workers(llm, do_sleep, level=sleep_level)
+        self._is_sleeping = True
+        gc_and_empty_cuda_cache()
+
+        # Reload training model to GPU (after vLLM is asleep)
+        self._state.reload_to_gpu()
+
+        # Load packed tensors
         packed_tensors = packed_tensors_from_dir(**disk_packed_tensors)
+
         # Wait for existing batches to finish
-        await self.results_queue.join()
+        await self._state.results_queue.join()
+
         # If we haven't already, start the training task
-        if self._train_task is None:
+        if not hasattr(self, "_train_task") or self._train_task is None:
             self._train_task = asyncio.create_task(
                 train(
-                    trainer=self.state.trainer,
-                    results_queue=self.results_queue,
+                    trainer=self._state.trainer,
+                    results_queue=self._state.results_queue,
                 )
             )
             warmup = True
         else:
             warmup = False
-        precalculate_logprobs = _config.get("precalculate_logprobs", False)
-        # Enter training mode
-        async with self.state.vllm.train_mode():
-            for offset in range(0, packed_tensors["tokens"].shape[0]):
-                for _ in range(2 if warmup else 1):
-                    if precalculate_logprobs and not warmup:
-                        packed_tensors["original_logprobs"] = packed_tensors["logprobs"]  # type: ignore
-                        packed_tensors["logprobs"] = torch.cat(
-                            [
-                                self.state.trainer.compute_loss(
-                                    self.state.peft_model,
-                                    TrainInputs(
-                                        **{
-                                            k: v[_offset : _offset + 1]
-                                            for k, v in packed_tensors.items()
-                                            if isinstance(v, torch.Tensor)
-                                        },
-                                        pixel_values=packed_tensors["pixel_values"][
-                                            _offset : _offset + 1
-                                        ],
-                                        image_grid_thw=packed_tensors["image_grid_thw"][
-                                            _offset : _offset + 1
-                                        ],
-                                        config=config,
-                                        _config=_config,
-                                        return_new_logprobs=True,
-                                    ),  # type: ignore
-                                )
-                                for _offset in range(
-                                    0, packed_tensors["tokens"].shape[0]
-                                )
-                            ]
-                        ).to("cpu")
-                        precalculate_logprobs = False
-                    self.state.inputs_queue.put_nowait(
-                        TrainInputs(
-                            **{
-                                k: (
-                                    v[offset : offset + 1, :1024]
-                                    if warmup and v.dim() > 1
-                                    else v[offset : offset + 1]
-                                )
-                                for k, v in packed_tensors.items()
-                                if isinstance(v, torch.Tensor)
-                            },
-                            pixel_values=(
-                                [None]
-                                if warmup
-                                else packed_tensors["pixel_values"][offset : offset + 1]
-                            ),
-                            image_grid_thw=(
-                                [None]
-                                if warmup
-                                else packed_tensors["image_grid_thw"][
-                                    offset : offset + 1
-                                ]
-                            ),
-                            config=(
-                                config.model_copy(
-                                    update={"lr": 1e-9, "beta": 0.0, "kl_coef": 0.0}
-                                )
-                                if warmup
-                                else config
-                            ),
-                            _config=_config,
-                            return_new_logprobs=False,
-                        )
-                    )
-                    # Wait for a result from the queue or for the training task to,
-                    # presumably, raise an exception
-                    done, _ = await asyncio.wait(
-                        [
-                            asyncio.create_task(self.results_queue.get()),
-                            self._train_task,
-                        ],
-                        return_when=asyncio.FIRST_COMPLETED,
-                    )
-                    if verbose:
-                        print(
-                            "Done waiting for a result from the queue or for the training task to, presumably, raise an exception"
-                        )
-                    for task in done:
-                        result = task.result()
-                        # If `result` is `None`, the training task finished somehow.
-                        assert result is not None, (
-                            "The training task should never finish."
-                        )
-                        self.results_queue.task_done()
-                        if warmup:
-                            from .state import gc_and_empty_cuda_cache
-
-                            gc_and_empty_cuda_cache()
-                            await asyncio.sleep(0.1)
-                            warmup = False
-                        else:
-                            yield result
-            if verbose:
-                print("Saving new LoRA adapter...")
-            # Save the new LoRA adapter
-            from ..utils.output_dirs import get_step_checkpoint_dir
-
-            next_step = get_step_from_dir(self.output_dir) + 1
-            checkpoint_dir = get_step_checkpoint_dir(self.output_dir, next_step)
-            os.makedirs(os.path.dirname(checkpoint_dir), exist_ok=True)
-            self.state.trainer.save_model(checkpoint_dir)
-            if verbose:
-                print("Setting new LoRA adapter...")
-            # Set the new LoRA adapter
-            self._set_lora(checkpoint_dir)
-            if verbose:
-                print("New LoRA adapter set")
 
-        if verbose:
-            print("ModelService.train complete")
-
-    def _set_lora(self, lora_path: str) -> None:
-        """Sets the LoRA adapter with ID 1 in the vLLM engine."""
-        from vllm.lora.request import LoRARequest
-
-        if hasattr(self.state.peft_model, "load_lora"):
-            lora_request: LoRARequest = self.state.peft_model.load_lora(
-                lora_path,
-                load_tensors=True,
-            )  # type: ignore
-            lora_request.lora_int_id = 1
-            lora_request.lora_name = self.model_name
-            lora_request.lora_path = lora_path
-        else:
-            lora_request = LoRARequest(
+        # Train on the batch using shared logic
+        async for result in process_train_batch(
+            packed_tensors=packed_tensors,
+            config=config,
+            _config=_config,
+            inputs_queue=self._state.inputs_queue,
+            results_queue=self._state.results_queue,
+            train_task=self._train_task,
+            trainer=self._state.trainer,
+            peft_model=self._state.peft_model,
+            warmup=warmup,
+            verbose=verbose,
+        ):
+            yield result
+
+        # Save checkpoint after training
+        checkpoint_dir = save_checkpoint(
+            trainer=self._state.trainer,
+            output_dir=self.output_dir,
+            verbose=verbose,
+        )
+
+        # Offload training model to CPU before waking vLLM
+        self._state.offload_to_cpu()
+
+        # Free memory before waking up vLLM
+        gc_and_empty_cuda_cache()
+        await asyncio.sleep(
+            0.5
+        )  # Longer delay to allow memory cleanup and pending ops to complete
+
+        # Wake up workers
+        await run_on_workers(llm, do_wake_up)
+        self._is_sleeping = False
+
+        # Swap out the LoRA adapter with the newly trained checkpoint
+        await llm.remove_lora(1)
+        await llm.add_lora(
+            LoRARequest(
                 lora_name=self.model_name,
                 lora_int_id=1,
-                lora_path=lora_path,
+                lora_path=checkpoint_dir,
+            )
+        )
+
+        # Resume generation after LoRA swap is complete
+        await llm.resume_generation()
+
+        if verbose:
+            print("UnslothService.train complete")
+
+    @cached_property
+    def _state(self) -> UnslothState:
+        import unsloth
+
+        # Initialize Unsloth model
+        init_args = self.config.get("init_args", {})
+        checkpoint_dir = get_last_checkpoint_dir(self.output_dir)
+        if checkpoint_dir:
+            init_args["model_name"] = checkpoint_dir
+        else:
+            init_args["model_name"] = self.base_model
+
+        model, tokenizer = cast(
+            tuple[CausalLM, PreTrainedTokenizerBase],
+            unsloth.FastLanguageModel.from_pretrained(**init_args),
+        )
+
+        # Initialize PEFT model - skip if already a PeftModel (e.g. loaded from checkpoint)
+        if (
+            hasattr(model, "peft_config")
+            and getattr(model, "peft_config", None) is not None
+        ):
+            # Model already has LoRA adapters (loaded from checkpoint)
+            peft_model = cast(peft.peft_model.PeftModelForCausalLM, model)
+        else:
+            peft_model = cast(
+                peft.peft_model.PeftModelForCausalLM,
+                unsloth.FastLanguageModel.get_peft_model(
+                    model, **self.config.get("peft_args", {})
+                ),
             )
-        self.state.vllm.async_engine.engine.remove_lora(1)
-        self.state.vllm.async_engine.engine.add_lora(lora_request)  # type: ignore
+
+        # Initialize trainer with dummy dataset
+        data = {"prompt": ""}
+        trainer = GRPOTrainer(
+            model=peft_model,  # type: ignore
+            reward_funcs=[],
+            args=GRPOConfig(**self.config.get("trainer_args", {})),  # type: ignore
+            train_dataset=Dataset.from_list([data for _ in range(10_000_000)]),
+            processing_class=tokenizer,
+        )
+
+        # Initialize queues
+        inputs_queue: asyncio.Queue[TrainInputs] = asyncio.Queue()
+        results_queue: asyncio.Queue[dict[str, float]] = asyncio.Queue()
+
+        # Patch trainer _prepare_inputs() to pull from queue
+        def _async_prepare_inputs(*_: Any, **__: Any) -> dict[str, torch.Tensor]:
+            async def get_inputs() -> TrainInputs:
+                return await inputs_queue.get()
+
+            # Force otherwise synchronous _prepare_inputs() to yield
+            # with nested asyncio.run() call
+            inputs = asyncio.run(get_inputs())
+
+            return cast(dict[str, torch.Tensor], inputs)
+
+        trainer._prepare_inputs = _async_prepare_inputs
+
+        return UnslothState(
+            model=model,
+            tokenizer=tokenizer,
+            peft_model=peft_model,
+            trainer=trainer,
+            inputs_queue=inputs_queue,
+            results_queue=results_queue,
+        )
+
+    @cached_property
+    def llm(self) -> asyncio.Task[AsyncLLM]:
+        # Filter engine args to remove incompatible boolean flags
+        engine_args = {
+            **self.config.get("engine_args", {}),
+            "enable_lora": True,
+        }
+        # Remove boolean flags that vLLM's argparse doesn't accept as =False
+        for key in ["enable_log_requests", "disable_log_requests"]:
+            engine_args.pop(key, None)
+        return asyncio.create_task(get_llm(AsyncEngineArgs(**engine_args)))
+
+
+# ============================================================================
+# Worker Sleep/Wake Functions
+# ============================================================================
+
+
+def do_sleep(*, level: int) -> None:
+    """
+    Put the worker to sleep, offloading both weights and KV cache.
+
+    Args:
+        level: The sleep level:
+            - 1: offload KV cache to CPU (can resume with existing KV state)
+            - 2: discard KV cache (fresh start after wake)
+    """
+    import ctypes
+    import gc
+
+    import torch
+    from vllm.device_allocator.cumem import (
+        CuMemAllocator,
+        libcudart,
+        unmap_and_release,
+    )
+
+    try:
+        from vllm.utils.platform_utils import is_pin_memory_available
+    except ImportError:
+        from vllm.utils import is_pin_memory_available
+
+    worker = get_worker()
+    allocator = CuMemAllocator.get_instance()
+
+    # Determine what to offload based on level:
+    # level=1: offload both weights and kv_cache to CPU
+    # level=2: offload weights, discard kv_cache
+    offload_to = "cpu" if level == 1 else "none"
+    tags_to_process = {"weights", "kv_cache"}
+
+    # Save buffers before level 2 sleep (like vLLM does)
+    if level == 2:
+        model = worker.model_runner.model
+        worker._sleep_saved_buffers = {
+            name: buffer.cpu().clone() for name, buffer in model.named_buffers()
+        }
+
+    for ptr, data in allocator.pointer_to_data.items():
+        if data.tag not in tags_to_process:
+            continue
+        handle = data.handle
+        size_in_bytes = handle[1]
+
+        # Always backup weights; backup kv_cache only at level 1
+        if offload_to != "none" or data.tag == "weights":
+            cpu_backup_tensor = torch.empty(
+                size_in_bytes,
+                dtype=torch.uint8,
+                device="cpu",
+                pin_memory=is_pin_memory_available(),
+            )
+            cpu_ptr = cpu_backup_tensor.data_ptr()
+            libcudart.cudaMemcpy(
+                ctypes.c_void_p(cpu_ptr), ctypes.c_void_p(ptr), size_in_bytes
+            )
+            data.cpu_backup_tensor = cpu_backup_tensor
+
+        unmap_and_release(handle)
+
+    gc.collect()
+    torch.cuda.empty_cache()
+
+
+def do_wake_up() -> None:
+    """
+    Wake up the worker from sleep, restoring offloaded weights and KV cache.
+    """
+    import ctypes
+
+    from vllm.device_allocator.cumem import (
+        CuMemAllocator,
+        create_and_map,
+        libcudart,
+    )
+
+    worker = get_worker()
+    allocator = CuMemAllocator.get_instance()
+
+    tags_to_process = {"weights", "kv_cache"}
+
+    for ptr, data in allocator.pointer_to_data.items():
+        if data.tag not in tags_to_process:
+            continue
+        create_and_map(data.handle)
+        if data.cpu_backup_tensor is not None:
+            cpu_backup_tensor = data.cpu_backup_tensor
+            size_in_bytes = cpu_backup_tensor.numel() * cpu_backup_tensor.element_size()
+            cpu_ptr = cpu_backup_tensor.data_ptr()
+            libcudart.cudaMemcpy(
+                ctypes.c_void_p(ptr),
+                ctypes.c_void_p(cpu_ptr),
+                size_in_bytes,
+            )
+            data.cpu_backup_tensor = None
+
+    # Restore buffers after level 2 sleep (like vLLM does)
+    if hasattr(worker, "_sleep_saved_buffers") and worker._sleep_saved_buffers:
+        model = worker.model_runner.model
+        for name, buffer in model.named_buffers():
+            if name in worker._sleep_saved_buffers:
+                buffer.copy_(worker._sleep_saved_buffers[name].to(buffer.device))
+        worker._sleep_saved_buffers = {}
diff --git a/src/art/unsloth/state.py b/src/art/unsloth/state.py
deleted file mode 100644
index b32f646db..000000000
--- a/src/art/unsloth/state.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import asyncio
-import os
-from contextlib import asynccontextmanager
-from dataclasses import replace
-from typing import TYPE_CHECKING, Any, AsyncGenerator, cast
-
-import nest_asyncio
-import peft
-import torch
-import unsloth  # type: ignore
-from datasets import Dataset
-from transformers.tokenization_utils_base import PreTrainedTokenizerBase
-from transformers.utils.dummy_pt_objects import (
-    GenerationMixin,
-    PreTrainedModel,
-)
-from trl import GRPOConfig, GRPOTrainer
-from vllm.engine.arg_utils import AsyncEngineArgs
-from vllm.engine.async_llm_engine import AsyncLLMEngine
-from vllm.worker.multi_step_model_runner import MultiStepModelRunner
-from vllm.worker.worker_base import WorkerWrapperBase
-
-from ..dev.model import InternalModelConfig
-from .train import gc_and_empty_cuda_cache
-
-if TYPE_CHECKING:
-    from .service import TrainInputs
-
-nest_asyncio.apply()
-
-
-class CausallLM(PreTrainedModel, GenerationMixin):
-    vllm_engine: AsyncLLMEngine
-
-
-class ModelState:
-    """
-    A class responsible for initializing and holding references to the model and related state.
-    """
-
-    def __init__(self, config: InternalModelConfig) -> None:
-        from vllm.engine import async_llm_engine
-
-        # Patch MultiStepModelRunner for Unsloth compatibility
-        if not hasattr(MultiStepModelRunner, "model"):
-            MultiStepModelRunner.model = property(  # type: ignore
-                lambda self: self._base_model_runner.model
-            )
-
-        # Set effectively unlimited timeout to support engine pausing & resumption
-        async_llm_engine.ENGINE_ITERATION_TIMEOUT_S = 2**31 - 1
-        # Sticking with V0 engine for now
-        os.environ["VLLM_USE_V1"] = "0"
-        # We can't use expandable segments with sleep mode
-        enable_sleep_mode = config.get("engine_args", {}).get(
-            "enable_sleep_mode", False
-        )
-        if enable_sleep_mode:
-            os.environ["PYTORCH_CUDA_ALLOC_CONF"] = ""
-        # We disable patching the v0 LoRA manager because it disables adapter loading
-        os.environ["UNSLOTH_DO_NOT_PATCH_V0_LRU_LORA_MANAGER"] = "1"
-        # Initialize Unsloth model
-        # NOTE: We have to patch empty_cache with a no-op during model initialization
-        # to avoid an allocator error.
-        empty_cache = torch.cuda.empty_cache
-        torch.cuda.empty_cache = lambda: None
-        from_engine_args = AsyncLLMEngine.from_engine_args
-
-        # NOTE: We also have to patch from_engine_args to control the engine args
-        # that are passed to the engine constructor.
-        def _from_engine_args(
-            engine_args: AsyncEngineArgs, *args: Any, **kwargs: Any
-        ) -> AsyncLLMEngine:
-            return from_engine_args(
-                replace(engine_args, **config.get("engine_args", {})), *args, **kwargs
-            )
-
-        AsyncLLMEngine.from_engine_args = _from_engine_args
-
-        self.model, self.tokenizer = cast(
-            tuple[CausallLM, PreTrainedTokenizerBase],
-            unsloth.FastLanguageModel.from_pretrained(**config.get("init_args", {})),
-        )
-        AsyncLLMEngine.from_engine_args = from_engine_args
-        torch.cuda.empty_cache = empty_cache
-        torch.cuda.empty_cache()
-        self.vllm = vLLMState(self.model.vllm_engine, enable_sleep_mode)
-        # Initialize PEFT model
-        if isinstance(self.model, peft.peft_model.PeftModelForCausalLM):
-            self.peft_model = self.model
-        else:
-            self.peft_model = cast(
-                peft.peft_model.PeftModelForCausalLM,
-                unsloth.FastLanguageModel.get_peft_model(
-                    self.model, **config.get("peft_args", {})
-                ),
-            )
-        # Initialize trainer
-        data = {"prompt": ""}
-        self.trainer = GRPOTrainer(
-            model=self.peft_model,  # type: ignore
-            reward_funcs=[],
-            args=GRPOConfig(**config.get("trainer_args", {})),  # type: ignore
-            train_dataset=Dataset.from_list([data for _ in range(10_000_000)]),
-            processing_class=self.tokenizer,
-        )
-        self.inputs_queue = asyncio.Queue["TrainInputs"]()
-
-        # Patch trainer _prepare_inputs()
-        def _async_prepare_inputs(*_, **__) -> dict[str, torch.Tensor]:
-            async def get_inputs() -> "TrainInputs":
-                return await self.inputs_queue.get()
-
-            # Force otherwise synchronous _prepare_inputs() to yield
-            # with nested asyncio.run() call
-            inputs = asyncio.run(get_inputs())
-
-            return cast(dict[str, torch.Tensor], inputs)
-
-        self.trainer._prepare_inputs = _async_prepare_inputs
-
-
-class vLLMState:
-    def __init__(self, async_engine: AsyncLLMEngine, enable_sleep_mode: bool) -> None:
-        from ..vllm import (
-            create_engine_pause_and_resume_functions,
-            patch_allocator,
-            patch_get_lora_tokenizer_async,
-            patch_lora_request,
-            patch_multi_step_model_runner,
-        )
-
-        if enable_sleep_mode:
-            patch_allocator()
-        # Unsloth patches
-        patch_lora_request()
-        patch_get_lora_tokenizer_async()
-        self.async_engine = async_engine
-        if enable_sleep_mode:
-            self.pause_engine, self.resume_engine = (
-                create_engine_pause_and_resume_functions(self.async_engine)
-            )
-        self.enable_sleep_mode = enable_sleep_mode
-        self.driver_worker = cast(
-            "WorkerWrapperBase",
-            getattr(self.async_engine.engine.model_executor, "driver_worker"),
-        )
-        if isinstance(self.driver_worker.model_runner, MultiStepModelRunner):
-            patch_multi_step_model_runner(self.driver_worker.model_runner)
-
-    @asynccontextmanager
-    async def train_mode(self) -> AsyncGenerator[None, None]:
-        """
-        A context manager pauses the vLLM engine and frees memory for training.
-        """
-        if not self.enable_sleep_mode:
-            yield
-            return
-        try:
-            await self.pause_engine()
-            try:
-                if self.async_engine.engine.has_unfinished_requests():
-                    # Offload KV cache to CPU memory (or disk)
-                    await self.async_engine.sleep(level=1)
-                else:
-                    # Reset prefix cache and discard KV cache
-                    await self.async_engine.reset_prefix_cache()
-                    await self.async_engine.sleep(level=2)
-                gc_and_empty_cuda_cache()
-                yield
-            finally:
-                gc_and_empty_cuda_cache()
-                await asyncio.sleep(0.1)
-                await self.async_engine.wake_up()
-        finally:
-            await self.resume_engine()
diff --git a/src/art/unsloth/train.py b/src/art/unsloth/train.py
index ddacaafdb..fba02d22e 100644
--- a/src/art/unsloth/train.py
+++ b/src/art/unsloth/train.py
@@ -1,13 +1,13 @@
 import asyncio
-import gc
-import os
 from collections import defaultdict
 from contextlib import nullcontext
+import gc
+import os
 from typing import TYPE_CHECKING, Callable, cast
 
 import nest_asyncio
-import torch
 from peft.peft_model import PeftModel
+import torch
 from trl import GRPOTrainer
 
 from .. import dev
diff --git a/src/art/utils/benchmarking/load_trajectories.py b/src/art/utils/benchmarking/load_trajectories.py
index f10127673..961e2c12d 100644
--- a/src/art/utils/benchmarking/load_trajectories.py
+++ b/src/art/utils/benchmarking/load_trajectories.py
@@ -11,6 +11,7 @@
 
 import duckdb
 from tqdm.auto import tqdm
+import yaml
 
 from art.utils.get_repo_root_path import get_repo_root_path
 from art.utils.output_dirs import (
diff --git a/src/art/utils/benchmarking/log_constant_metrics_wandb.py b/src/art/utils/benchmarking/log_constant_metrics_wandb.py
index 778aa87d5..7f876a191 100644
--- a/src/art/utils/benchmarking/log_constant_metrics_wandb.py
+++ b/src/art/utils/benchmarking/log_constant_metrics_wandb.py
@@ -1,8 +1,9 @@
 """Utilities for logging constant baseline metrics to Weights & Biases."""
 
-import art
 import wandb
 
+import art
+
 
 async def log_constant_metrics_wandb(
     model: art.Model,
diff --git a/src/art/utils/deployment/together.py b/src/art/utils/deployment/together.py
index a82f48c2e..b2049279c 100644
--- a/src/art/utils/deployment/together.py
+++ b/src/art/utils/deployment/together.py
@@ -1,10 +1,10 @@
 """Together deployment functionality."""
 
 import asyncio
+from enum import Enum
 import json
 import os
 import time
-from enum import Enum
 from typing import TYPE_CHECKING, Any
 
 import aiohttp
diff --git a/src/art/utils/format_message.py b/src/art/utils/format_message.py
index 8f0d27ab0..874eb6ec9 100644
--- a/src/art/utils/format_message.py
+++ b/src/art/utils/format_message.py
@@ -1,3 +1,7 @@
+from openai.types.chat.chat_completion_message_function_tool_call import (
+    ChatCompletionMessageFunctionToolCall,
+)
+
 from ..types import Message
 
 
@@ -12,6 +16,7 @@ def format_message(message: Message) -> str:
     tool_calls_text += "\n".join(
         f"{tool_call['function']['name']}({tool_call['function']['arguments']})"
         for tool_call in message.get("tool_calls") or []
+        if "function" in tool_call
     )
 
     # Combine all parts
diff --git a/src/art/utils/iterate_dataset.py b/src/art/utils/iterate_dataset.py
index fda51c41b..6b546e79e 100644
--- a/src/art/utils/iterate_dataset.py
+++ b/src/art/utils/iterate_dataset.py
@@ -1,6 +1,6 @@
+from dataclasses import dataclass
 import math
 import random
-from dataclasses import dataclass
 from typing import Generator, Generic, List, TypeVar
 
 from tqdm.auto import tqdm
diff --git a/src/art/utils/old_benchmarking/generate_line_graphs.py b/src/art/utils/old_benchmarking/generate_line_graphs.py
index ad809e42c..a0612c19d 100644
--- a/src/art/utils/old_benchmarking/generate_line_graphs.py
+++ b/src/art/utils/old_benchmarking/generate_line_graphs.py
@@ -1,5 +1,5 @@
-import os
 from datetime import datetime
+import os
 from typing import Literal
 
 try:
diff --git a/src/art/utils/s3.py b/src/art/utils/s3.py
index 61a016bc3..a96acf19d 100644
--- a/src/art/utils/s3.py
+++ b/src/art/utils/s3.py
@@ -1,11 +1,11 @@
 from __future__ import annotations
 
 import asyncio
+from asyncio.subprocess import DEVNULL
 import os
 import tempfile
-import zipfile
-from asyncio.subprocess import DEVNULL
 from typing import Literal, Optional, Sequence
+import zipfile
 
 from art.errors import ForbiddenBucketCreationError
 from art.utils.output_dirs import (
diff --git a/src/art/utils/trajectory_logging.py b/src/art/utils/trajectory_logging.py
index 05b30d66e..d7009f2d1 100644
--- a/src/art/utils/trajectory_logging.py
+++ b/src/art/utils/trajectory_logging.py
@@ -9,11 +9,16 @@
 
 import json
 from pathlib import Path
+from typing import Any
+
+from litellm.types.utils import Choices
+from openai.types.chat.chat_completion import Choice
+from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
 
 from art import Trajectory, TrajectoryGroup
 
 
-def _flatten_message(msg) -> dict:
+def _flatten_message(msg: dict) -> dict:
     """Convert a message or Choice to flat parquet format."""
     if "finish_reason" in msg:
         # Choice format - extract inner message, mark as trainable
@@ -73,20 +78,20 @@ def write_trajectory_groups_parquet(
 
             # Flatten messages
             messages = []
-            for msg in trajectory.messages_and_choices:
-                # Handle both dict and Choice/Message objects
-                if hasattr(msg, "to_dict"):
-                    msg = msg.to_dict()
-                elif hasattr(msg, "finish_reason"):
-                    # Choice object - convert to dict format
-                    msg = {
-                        "finish_reason": msg.finish_reason,
-                        "index": msg.index,
-                        "message": msg.message.to_dict()
-                        if hasattr(msg.message, "to_dict")
-                        else msg.message,
+            for message_or_choice in trajectory.messages_and_choices:
+                if isinstance(message_or_choice, Choice):
+                    message = message_or_choice.to_dict()
+                elif isinstance(message_or_choice, Choices):
+                    message = {
+                        "finish_reason": message_or_choice.finish_reason,
+                        "index": message_or_choice.index,
+                        "message": message_or_choice.message.to_dict()
+                        if hasattr(message_or_choice.message, "to_dict")
+                        else message_or_choice.message,
                     }
-                messages.append(_flatten_message(msg))
+                else:
+                    message = message_or_choice
+                messages.append(_flatten_message(message))  # type: ignore
 
             rows.append(
                 {
diff --git a/src/art/utils/trajectory_migration.py b/src/art/utils/trajectory_migration.py
index 823ea0da4..64841f652 100644
--- a/src/art/utils/trajectory_migration.py
+++ b/src/art/utils/trajectory_migration.py
@@ -10,8 +10,8 @@
 The migration provides ~25x compression and ~20x faster query performance.
 """
 
-import json
 from dataclasses import dataclass, field
+import json
 from pathlib import Path
 from typing import Any, Callable, Iterator, cast
 
diff --git a/src/art/vllm/__init__.py b/src/art/vllm/__init__.py
index 8d14ddfe3..9ae9c5efb 100644
--- a/src/art/vllm/__init__.py
+++ b/src/art/vllm/__init__.py
@@ -4,7 +4,6 @@
 # Engine and worker management
 from .engine import (
     WorkerExtension,
-    create_engine_pause_and_resume_functions,
     get_llm,
     get_worker,
     run_on_workers,
@@ -12,11 +11,7 @@
 
 # Patches - these are typically imported for their side effects
 from .patches import (
-    patch_allocator,
-    patch_get_lora_tokenizer_async,
     patch_listen_for_disconnect,
-    patch_lora_request,
-    patch_multi_step_model_runner,
     patch_tool_parser_manager,
     subclass_chat_completion_request,
 )
@@ -33,16 +28,11 @@
     "set_vllm_log_file",
     # Engine
     "get_llm",
-    "create_engine_pause_and_resume_functions",
     "run_on_workers",
     "get_worker",
     "WorkerExtension",
     # Patches
-    "patch_allocator",
     "subclass_chat_completion_request",
-    "patch_lora_request",
-    "patch_get_lora_tokenizer_async",
     "patch_listen_for_disconnect",
     "patch_tool_parser_manager",
-    "patch_multi_step_model_runner",
 ]
diff --git a/src/art/vllm/engine.py b/src/art/vllm/engine.py
index 7bba9f0e0..46843d003 100644
--- a/src/art/vllm/engine.py
+++ b/src/art/vllm/engine.py
@@ -3,19 +3,16 @@
 import asyncio
 import contextlib
 import contextvars
+from dataclasses import replace
 import os
 import time
-from dataclasses import replace
-from typing import Any, Callable, Coroutine, Generator, ParamSpec, TypeVar, cast
+from typing import Any, Callable, Generator, ParamSpec, TypeVar, cast
 
 import cloudpickle
 import vllm
-from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.v1.engine.async_llm import AsyncLLM
 from vllm.v1.worker.gpu_worker import Worker
 
-from .patches import patch_allocator
-
 
 async def get_llm(args: vllm.AsyncEngineArgs) -> AsyncLLM:
     """
@@ -34,11 +31,6 @@ async def get_llm(args: vllm.AsyncEngineArgs) -> AsyncLLM:
         )
         await process.wait()
 
-    # Make sure we are using the V1 engine
-    import vllm.envs as envs
-
-    envs.VLLM_USE_V1 = True
-
     llm = AsyncLLM.from_engine_args(
         replace(
             args,
@@ -46,49 +38,9 @@ async def get_llm(args: vllm.AsyncEngineArgs) -> AsyncLLM:
             enable_sleep_mode=True,
         )
     )
-    await run_on_workers(llm, patch_allocator)
     return llm
 
 
-def create_engine_pause_and_resume_functions(
-    engine: AsyncLLMEngine,
-) -> tuple[
-    Callable[[], Coroutine[Any, Any, None]], Callable[[], Coroutine[Any, Any, None]]
-]:
-    """
-    Patches the vLLM engine and returns a pair of functions for pausing and resuming
-    request processing respectively.
-
-    Args:
-        engine: The AsyncLLMEngine to patch.
-
-    Returns:
-        A tuple of (pause_engine, resume_engine) async functions.
-    """
-    _engine_step = engine.engine_step
-    resume_event = asyncio.Event()
-    resume_event.set()
-    engine_step_event = asyncio.Event()
-
-    async def engine_step(virtual_engine: int) -> bool:
-        engine_step_event.set()
-        await resume_event.wait()
-        return await _engine_step(virtual_engine)
-
-    engine.engine_step = engine_step
-
-    async def pause_engine() -> None:
-        resume_event.clear()
-        if engine.engine.has_unfinished_requests():
-            engine_step_event.clear()
-            await engine_step_event.wait()
-
-    async def resume_engine() -> None:
-        resume_event.set()
-
-    return pause_engine, resume_engine
-
-
 P = ParamSpec("P")
 R = TypeVar("R")
 
diff --git a/src/art/vllm/patches.py b/src/art/vllm/patches.py
index 2fc48f1cf..54d2eba89 100644
--- a/src/art/vllm/patches.py
+++ b/src/art/vllm/patches.py
@@ -1,98 +1,7 @@
 """Monkey patches and modifications for vLLM."""
 
-import ctypes
 from typing import Any
 
-import torch
-from vllm.worker.multi_step_model_runner import MultiStepModelRunner
-
-
-def patch_allocator() -> None:
-    """
-    Patch the vLLM CuMemAllocator to specifically focus on offloading/discarding
-    the KV cache.
-    """
-    from vllm.device_allocator.cumem import (
-        CuMemAllocator,
-        create_and_map,
-        libcudart,
-        unmap_and_release,
-    )
-    from vllm.utils import is_pin_memory_available
-
-    allocator = CuMemAllocator.get_instance()
-
-    def sleep(offload_tags: tuple[str, ...] | str | None = None) -> None:
-        # In this version of vLLM (0.7.3) one tag is provided for sleep level 1
-        # and no tags are provided for sleep level 2, so we can reverse-engineer
-        # the sleep level from the tags
-        sleep_level = 1 if offload_tags else 2
-        # We reinterpret the sleep levels as follows:
-        # Sleep level 1: offload kv cache to CPU memory (or disk)
-        if sleep_level == 1:
-            offload_to = "cpu"
-            # TODO: Check if there is sufficient CPU memory, otherwise offload to disk
-        # Sleep level 2: discard kv cache
-        else:
-            offload_to = "none"
-
-        override_tags = getattr(allocator, "_override_tags", {"kv_cache"})
-        for ptr, data in allocator.pointer_to_data.items():
-            if data.tag not in override_tags:
-                continue
-            handle = data.handle
-            size_in_bytes = handle[1]
-            if offload_to != "none" or data.tag == "weights":
-                if offload_to == "disk" and data.tag != "weights":
-                    cpu_backup_tensor = torch.from_file(
-                        f"/tmp/kv-cache-{ptr}.pt",
-                        size=size_in_bytes,
-                        dtype=torch.uint8,
-                        device="cpu",
-                        shared=True,
-                    )
-                else:
-                    cpu_backup_tensor = torch.empty(
-                        size_in_bytes,
-                        dtype=torch.uint8,
-                        device="cpu",
-                        pin_memory=is_pin_memory_available(),
-                    )
-                cpu_ptr = cpu_backup_tensor.data_ptr()
-                libcudart.cudaMemcpy(
-                    ctypes.c_void_p(cpu_ptr), ctypes.c_void_p(ptr), size_in_bytes
-                )
-                data.cpu_backup_tensor = cpu_backup_tensor
-            unmap_and_release(handle)
-
-    def wake_up(tags: list[str] | None = None) -> None:
-        """
-        Wake up the allocator from sleep mode.
-        All data that is previously offloaded will be loaded back to GPU
-        memory, and the rest of the data will have empty memory.
-        """
-        override_tags = getattr(allocator, "_override_tags", {"kv_cache"})
-        for ptr, data in allocator.pointer_to_data.items():
-            if data.tag not in override_tags:
-                continue
-            create_and_map(data.handle)
-            if data.cpu_backup_tensor is not None:
-                cpu_backup_tensor = data.cpu_backup_tensor
-                if cpu_backup_tensor is not None:
-                    size_in_bytes = (
-                        cpu_backup_tensor.numel() * cpu_backup_tensor.element_size()
-                    )
-                    cpu_ptr = cpu_backup_tensor.data_ptr()
-                    libcudart.cudaMemcpy(
-                        ctypes.c_void_p(ptr),
-                        ctypes.c_void_p(cpu_ptr),
-                        size_in_bytes,
-                    )
-                    data.cpu_backup_tensor = None
-
-    allocator.sleep = sleep
-    allocator.wake_up = wake_up
-
 
 def subclass_chat_completion_request() -> None:
     """
@@ -110,38 +19,6 @@ def __init__(self, *args: object, **kwargs: object) -> None:
     vllm.entrypoints.openai.protocol.ChatCompletionRequest = ChatCompletionRequest
 
 
-def patch_lora_request() -> None:
-    """
-    Patches the vLLM LoRARequest type to have attributes Unsloth expects and the Unsloth LoRARequest type to have attributes vLLM expects.
-    """
-    from unsloth_zoo.vllm_lora_request import LoRARequest as UnslothLoRARequest
-    from vllm.lora.request import LoRARequest
-
-    LoRARequest.lora_tensors = {}  # type: ignore
-    LoRARequest.lora_embeddings = {}  # type: ignore
-    UnslothLoRARequest.tensorizer_config_dict = None  # type: ignore
-
-
-def patch_get_lora_tokenizer_async() -> None:
-    import vllm.transformers_utils.tokenizer
-    import vllm.transformers_utils.tokenizer_group
-
-    async def patch(*_: Any, **__: Any) -> None:
-        return None
-
-    vllm.transformers_utils.tokenizer.get_lora_tokenizer_async = patch  # type: ignore
-    vllm.transformers_utils.tokenizer_group.get_lora_tokenizer_async = (  # type: ignore
-        patch
-    )
-
-    async def patch2(self, *args: Any, **kwargs: Any) -> None:
-        return self.tokenizer
-
-    vllm.transformers_utils.tokenizer_group.TokenizerGroup.get_lora_tokenizer_async = (
-        patch2  # type: ignore
-    )
-
-
 def patch_listen_for_disconnect() -> None:
     async def patched_listen_for_disconnect(request):
         try:
@@ -163,9 +40,7 @@ def patch_tool_parser_manager() -> None:
     Patch ToolParserManager to support streaming tool call logprobs.
     """
     from vllm.entrypoints.openai.protocol import DeltaMessage
-    from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
-        ToolParserManager,
-    )
+    from vllm.tool_parsers.abstract_tool_parser import ToolParserManager
 
     get_tool_parser = ToolParserManager.get_tool_parser
 
@@ -183,15 +58,3 @@ def patch(
         return tool_parser_class
 
     ToolParserManager.get_tool_parser = patched_get_tool_parser
-
-
-def patch_multi_step_model_runner(runner: MultiStepModelRunner) -> None:
-    """
-    Patches a MultiStepModelRunner to support LoRA adapters.
-    """
-    base_runner = runner._base_model_runner
-    runner.set_active_loras = base_runner.set_active_loras
-    runner.add_lora = base_runner.add_lora
-    runner.remove_lora = base_runner.remove_lora
-    runner.pin_lora = base_runner.pin_lora
-    runner.list_loras = base_runner.list_loras
diff --git a/src/art/vllm/server.py b/src/art/vllm/server.py
index d3559fe5f..90e2f7e33 100644
--- a/src/art/vllm/server.py
+++ b/src/art/vllm/server.py
@@ -1,9 +1,9 @@
 """OpenAI-compatible server functionality for vLLM."""
 
 import asyncio
+from contextlib import asynccontextmanager
 import logging
 import os
-from contextlib import asynccontextmanager
 from typing import Any, AsyncIterator, Coroutine
 
 from openai import AsyncOpenAI
@@ -11,7 +11,8 @@
 from vllm.engine.protocol import EngineClient
 from vllm.entrypoints.openai.cli_args import make_arg_parser, validate_parsed_serve_args
 from vllm.logger import _DATE_FORMAT, _FORMAT
-from vllm.utils import FlexibleArgumentParser
+from vllm.logging_utils import NewLineFormatter
+from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 from ..dev.openai_server import OpenAIServerConfig
 
@@ -47,20 +48,24 @@ async def openai_server_task(
     patch_tool_parser_manager()
     set_vllm_log_file(config.get("log_file", "vllm.log"))
 
-    # Patch engine.add_lora; hopefully temporary
+    # Patch engine.add_lora to ensure lora_tensors attribute exists
+    # This is needed for compatibility with Unsloth
     add_lora = engine.add_lora
 
-    async def _add_lora(lora_request) -> None:
-        class LoRARequest:
-            def __getattr__(self, name: str) -> Any:
-                if name == "lora_tensors" and not hasattr(lora_request, name):
-                    return None
-                return getattr(lora_request, name)
-
-            def __setattr__(self, name: str, value: Any) -> None:
-                setattr(lora_request, name, value)
-
-        await add_lora(LoRARequest())  # type: ignore
+    async def _add_lora(lora_request) -> bool:
+        # Ensure lora_tensors attribute exists on the request
+        if not hasattr(lora_request, "lora_tensors"):
+            # For msgspec.Struct, we need to create a new instance with the attribute
+            from vllm.lora.request import LoRARequest
+
+            lora_request = LoRARequest(
+                lora_name=lora_request.lora_name,
+                lora_int_id=lora_request.lora_int_id,
+                lora_path=lora_request.lora_path,
+                long_lora_max_len=getattr(lora_request, "long_lora_max_len", None),
+                base_model_name=getattr(lora_request, "base_model_name", None),
+            )
+        return await add_lora(lora_request)
 
     engine.add_lora = _add_lora
 
@@ -177,8 +182,8 @@ def set_vllm_log_file(path: str) -> None:
     # Create a file handler
     file_handler = logging.FileHandler(path)
 
-    # Use the same formatter as vLLM's default
-    formatter = logging.Formatter(_FORMAT, _DATE_FORMAT)
+    # Use vLLM's NewLineFormatter which adds the fileinfo field
+    formatter = NewLineFormatter(fmt=_FORMAT, datefmt=_DATE_FORMAT)
     file_handler.setFormatter(formatter)
 
     # Add the handler to the logger
diff --git a/src/mp_actors/move.py b/src/mp_actors/move.py
index 2e387a561..3952fdda1 100644
--- a/src/mp_actors/move.py
+++ b/src/mp_actors/move.py
@@ -1,12 +1,12 @@
 import asyncio
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass
 import inspect
 import multiprocessing as mp
 import os
 import sys
-import uuid
-from concurrent.futures import ThreadPoolExecutor
-from dataclasses import dataclass
 from typing import Any, AsyncGenerator, TypeVar, cast
+import uuid
 
 import nest_asyncio
 import setproctitle
diff --git a/tests/integration.py b/tests/integration.py
index 07191a7da..43a60c09a 100644
--- a/tests/integration.py
+++ b/tests/integration.py
@@ -1,14 +1,14 @@
 import argparse
 import ast
+from pathlib import Path
 import re
 import sys
-from pathlib import Path
 from typing import Any, Dict
 
-import nbformat
-import pytest
 from nbclient import NotebookClient
 from nbclient.exceptions import CellExecutionError
+import nbformat
+import pytest
 
 NOTEBOOKS = [
     {
diff --git a/tests/unit/test_auto_trajectory.py b/tests/unit/test_auto_trajectory.py
index af170ad77..d70243bd7 100644
--- a/tests/unit/test_auto_trajectory.py
+++ b/tests/unit/test_auto_trajectory.py
@@ -3,16 +3,16 @@
 # Suppress pydantic warnings at module level
 warnings.filterwarnings("ignore", category=DeprecationWarning, module="pydantic")
 
+from aiohttp import web
 import litellm
 import litellm.litellm_core_utils.streaming_handler
 import litellm.types.utils
-import pytest
-import pytest_asyncio
-from aiohttp import web
 from openai import AsyncOpenAI
 from openai.types.chat.chat_completion import Choice
 from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
 from openai.types.chat.chat_completion_tool_param import ChatCompletionToolParam
+import pytest
+import pytest_asyncio
 
 import art
 from art.utils.litellm import convert_litellm_choice_to_openai
diff --git a/tests/unit/test_tokenize_trajectory_groups.ipynb b/tests/unit/test_tokenize_trajectory_groups.ipynb
index a5fdad5e1..0628de3ee 100644
--- a/tests/unit/test_tokenize_trajectory_groups.ipynb
+++ b/tests/unit/test_tokenize_trajectory_groups.ipynb
@@ -4,7 +4,14 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "93a238e4",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-01T20:09:38.111246Z",
+     "iopub.status.busy": "2026-01-01T20:09:38.111147Z",
+     "iopub.status.idle": "2026-01-01T20:09:38.121135Z",
+     "shell.execute_reply": "2026-01-01T20:09:38.120708Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%load_ext autoreload\n",
@@ -15,6 +22,32 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "84717d3b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-01-01T20:09:38.122316Z",
+     "iopub.status.busy": "2026-01-01T20:09:38.122204Z",
+     "iopub.status.idle": "2026-01-01T20:09:43.817637Z",
+     "shell.execute_reply": "2026-01-01T20:09:43.817009Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "from openai.types.chat.chat_completion import Choice, ChoiceLogprobs\n",
+    "from openai.types.chat.chat_completion_message import ChatCompletionMessage\n",
+    "from openai.types.chat.chat_completion_token_logprob import ChatCompletionTokenLogprob\n",
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "import art\n",
+    "from art.preprocessing.tokenize import tokenize_trajectory_groups\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"Qwen/Qwen2.5-7B-Instruct\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "388bd997",
    "metadata": {},
    "outputs": [
     {
@@ -37,20 +70,6 @@
     }
    ],
    "source": [
-    "from IPython.display import clear_output\n",
-    "from openai.types.chat.chat_completion import Choice, ChoiceLogprobs\n",
-    "from openai.types.chat.chat_completion_message import ChatCompletionMessage\n",
-    "from openai.types.chat.chat_completion_token_logprob import ChatCompletionTokenLogprob\n",
-    "from transformers import AutoTokenizer\n",
-    "from transformers.utils.logging import disable_progress_bar\n",
-    "\n",
-    "import art\n",
-    "from art.preprocessing.tokenize import tokenize_trajectory_groups\n",
-    "\n",
-    "disable_progress_bar()\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"Qwen/Qwen2.5-7B-Instruct\")\n",
-    "\n",
     "tokenized_results = list(\n",
     "    tokenize_trajectory_groups(\n",
     "        tokenizer,\n",
@@ -127,7 +146,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/tests/unit/test_trajectory_copy.py b/tests/unit/test_trajectory_copy.py
index 92f5b97a0..a11f46bb2 100644
--- a/tests/unit/test_trajectory_copy.py
+++ b/tests/unit/test_trajectory_copy.py
@@ -2,9 +2,9 @@
 
 import copy
 
-import pytest
 from openai.types.chat import ChatCompletionMessage
 from openai.types.chat.chat_completion import Choice
+import pytest
 
 from art.trajectories import PydanticException, Trajectory, TrajectoryGroup
 
diff --git a/tests/unit/test_trajectory_parquet.py b/tests/unit/test_trajectory_parquet.py
index 5c9c47dd6..dbcef3bbb 100644
--- a/tests/unit/test_trajectory_parquet.py
+++ b/tests/unit/test_trajectory_parquet.py
@@ -11,13 +11,32 @@
 
 import json
 import os
+from pathlib import Path
 import shutil
 import tempfile
-from pathlib import Path
+from typing import cast
 
+from openai.types.chat.chat_completion import Choice
+from openai.types.chat.chat_completion_assistant_message_param import (
+    ChatCompletionAssistantMessageParam,
+)
+from openai.types.chat.chat_completion_message_function_tool_call_param import (
+    ChatCompletionMessageFunctionToolCallParam,
+)
+from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
+from openai.types.chat.chat_completion_message_tool_call_union_param import (
+    ChatCompletionMessageToolCallUnionParam,
+)
+from openai.types.chat.chat_completion_tool_message_param import (
+    ChatCompletionToolMessageParam,
+)
+from openai.types.chat.chat_completion_user_message_param import (
+    ChatCompletionUserMessageParam,
+)
 import pytest
 
 from art import Trajectory, TrajectoryGroup
+from art.types import MessageOrChoice
 from art.utils.trajectory_logging import (
     read_trajectory_groups_parquet,
     write_trajectory_groups_parquet,
@@ -36,6 +55,32 @@
 FIXTURES_DIR = Path(__file__).parent.parent / "fixtures" / "trajectories"
 
 
+def _ensure_message(item: MessageOrChoice) -> ChatCompletionMessageParam:
+    """Narrow a trajectory entry to a concrete message (not a Choice)."""
+    assert not isinstance(item, Choice)
+    return cast(ChatCompletionMessageParam, item)
+
+
+def _ensure_assistant_message(
+    item: MessageOrChoice,
+) -> ChatCompletionAssistantMessageParam:
+    msg = _ensure_message(item)
+    assert msg["role"] == "assistant"
+    return cast(ChatCompletionAssistantMessageParam, msg)
+
+
+def _ensure_tool_message(item: MessageOrChoice) -> ChatCompletionToolMessageParam:
+    msg = _ensure_message(item)
+    assert msg["role"] == "tool"
+    return cast(ChatCompletionToolMessageParam, msg)
+
+
+def _ensure_user_message(item: MessageOrChoice) -> ChatCompletionUserMessageParam:
+    msg = _ensure_message(item)
+    assert msg["role"] == "user"
+    return cast(ChatCompletionUserMessageParam, msg)
+
+
 class TestParquetRoundTrip:
     """Test that data survives write -> read cycle."""
 
@@ -120,12 +165,19 @@ def test_tool_calls(self, tmp_path: Path):
         assert len(traj.messages_and_choices) == 3
 
         # Check tool call message
-        tool_call_msg = traj.messages_and_choices[1]
-        assert "tool_calls" in tool_call_msg
-        assert tool_call_msg["tool_calls"][0]["function"]["name"] == "search"
+        tool_call_msg = _ensure_assistant_message(traj.messages_and_choices[1])
+        tool_calls = cast(
+            list[ChatCompletionMessageToolCallUnionParam],
+            list(tool_call_msg.get("tool_calls") or []),
+        )
+        assert tool_calls, "Assistant message should include tool calls"
+        first_call = tool_calls[0]
+        assert first_call["type"] == "function"
+        function_call = cast(ChatCompletionMessageFunctionToolCallParam, first_call)
+        assert function_call["function"]["name"] == "search"
 
         # Check tool result message
-        tool_result_msg = traj.messages_and_choices[2]
+        tool_result_msg = _ensure_tool_message(traj.messages_and_choices[2])
         assert tool_result_msg["tool_call_id"] == "call_123"
 
     def test_choice_format(self, tmp_path: Path):
@@ -163,14 +215,18 @@ def test_choice_format(self, tmp_path: Path):
         traj = loaded[0].trajectories[0]
         # Choice format is flattened to a simple message dict
         # The inner message content is preserved
-        user_msg = traj.messages_and_choices[0]
-        assistant_msg = traj.messages_and_choices[1]
+        user_msg = _ensure_user_message(traj.messages_and_choices[0])
+        assistant_msg = _ensure_assistant_message(traj.messages_and_choices[1])
 
         assert user_msg["role"] == "user"
-        assert user_msg["content"] == "Hello"
+        user_content = user_msg["content"]
+        assert isinstance(user_content, str)
+        assert user_content == "Hello"
 
         assert assistant_msg["role"] == "assistant"
-        assert assistant_msg["content"] == "Hi!"
+        assistant_content = assistant_msg.get("content")
+        assert isinstance(assistant_content, str)
+        assert assistant_content == "Hi!"
 
     def test_unicode_content(self, tmp_path: Path):
         """Test trajectories with unicode and special characters."""
@@ -200,8 +256,11 @@ def test_unicode_content(self, tmp_path: Path):
         loaded = read_trajectory_groups_parquet(parquet_path)
 
         traj = loaded[0].trajectories[0]
-        assert "你好" in traj.messages_and_choices[0]["content"]
-        assert "🎉" in traj.messages_and_choices[0]["content"]
+        user_msg = _ensure_user_message(traj.messages_and_choices[0])
+        user_content = user_msg["content"]
+        assert isinstance(user_content, str)
+        assert "你好" in user_content
+        assert "🎉" in user_content
         assert "中文日志" in traj.logs[0]
 
     def test_multiple_trajectories(self, tmp_path: Path):
diff --git a/tests/unit/test_yield_trajectory.py b/tests/unit/test_yield_trajectory.py
index c730c4e71..c7114051b 100644
--- a/tests/unit/test_yield_trajectory.py
+++ b/tests/unit/test_yield_trajectory.py
@@ -1,8 +1,8 @@
-import pytest_asyncio
 from aiohttp import web
 from openai import AsyncOpenAI
 from openai.types.chat.chat_completion import Choice
 from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
+import pytest_asyncio
 
 import art
 
diff --git a/uv.lock b/uv.lock
index 71716196c..470516052 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,20 +2,30 @@ version = 1
 revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
-    "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
-    "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and sys_platform == 'linux'",
     "python_full_version == '3.12.*' and sys_platform == 'linux'",
-    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform != 'linux'",
-    "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform != 'linux'",
-    "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'linux'",
-    "python_full_version == '3.12.*' and sys_platform != 'linux'",
     "python_full_version == '3.11.*' and sys_platform == 'linux'",
     "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version >= '3.14' and sys_platform != 'linux'",
+    "python_full_version == '3.13.*' and sys_platform != 'linux'",
+    "python_full_version == '3.12.*' and sys_platform != 'linux'",
     "python_full_version == '3.11.*' and sys_platform != 'linux'",
     "python_full_version < '3.11' and sys_platform != 'linux'",
 ]
 
+[[package]]
+name = "abnf"
+version = "2.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9d/f2/7b5fac50ee42e8b8d4a098d76743a394546f938c94125adbb93414e5ae7d/abnf-2.2.0.tar.gz", hash = "sha256:433380fd32855bbc60bc7b3d35d40616e21383a32ed1c9b8893d16d9f4a6c2f4", size = 197507, upload-time = "2023-03-17T18:26:24.577Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/30/95/f456ae7928a2f3a913f467d4fd9e662e295dd7349fc58b35f77f6c757a23/abnf-2.2.0-py3-none-any.whl", hash = "sha256:5dc2ae31a84ff454f7de46e08a2a21a442a0e21a092468420587a1590b490d1f", size = 39938, upload-time = "2023-03-17T18:26:22.608Z" },
+]
+
 [[package]]
 name = "accelerate"
 version = "1.7.0"
@@ -36,14 +46,14 @@ wheels = [
 
 [[package]]
 name = "aiodns"
-version = "3.6.0"
+version = "3.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pycares" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e6/11/238e97cbf5c1c0f725d590a092e0618dcdc50f44dbd1e2a926fae27e6f06/aiodns-3.6.0.tar.gz", hash = "sha256:9b0ef54339e6687cdbd39a7d73d7de2467cb5c115281da28f2598f058633dac8", size = 15108, upload-time = "2025-12-05T18:29:57.873Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/85/2f/9d1ee4f937addda60220f47925dac6c6b3782f6851fd578987284a8d2491/aiodns-3.6.1.tar.gz", hash = "sha256:b0e9ce98718a5b8f7ca8cd16fc393163374bc2412236b91f6c851d066e3324b6", size = 15143, upload-time = "2025-12-11T12:53:07.785Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/dc/ae/10adb8d683bdb800ad6ae1460d2099db7045a67eff13985955a72448e8c1/aiodns-3.6.0-py3-none-any.whl", hash = "sha256:9fa49f812648374d046436a3b96491afb141320cd0b501df83880f034d0b5b85", size = 7971, upload-time = "2025-12-05T18:29:56.228Z" },
+    { url = "https://files.pythonhosted.org/packages/09/e3/9f777774ebe8f664bcd564f9de3936490a16effa82a969372161c9b0fb21/aiodns-3.6.1-py3-none-any.whl", hash = "sha256:46233ccad25f2037903828c5d05b64590eaa756e51d12b4a5616e2defcbc98c7", size = 7975, upload-time = "2025-12-11T12:53:06.387Z" },
 ]
 
 [[package]]
@@ -66,7 +76,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.12.15"
+version = "3.13.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -78,81 +88,116 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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-wheels = [
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 [[package]]
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 source = { registry = "https://pypi.org/simple" }
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