Instructions to use OpenRubrics/ReVeR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenRubrics/ReVeR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenRubrics/ReVeR")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenRubrics/ReVeR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenRubrics/ReVeR with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenRubrics/ReVeR" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenRubrics/ReVeR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OpenRubrics/ReVeR
- SGLang
How to use OpenRubrics/ReVeR with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "OpenRubrics/ReVeR" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenRubrics/ReVeR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "OpenRubrics/ReVeR" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenRubrics/ReVeR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OpenRubrics/ReVeR with Docker Model Runner:
docker model run hf.co/OpenRubrics/ReVeR
File size: 12,066 Bytes
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tests). The code runs in a separate process with a time limit, a memory limit, a whitelist of builtins and modules,
and the task (instruction, response, rubrics) bound as variables. The reply is a JSON string with the keys ok,
stdout, result and error; `result` is the repr of a variable named `result`, if the code sets one."""
from __future__ import annotations
import ast
import contextlib
import json
import multiprocessing as mp
import re
import reprlib
import signal
import time
import traceback
from dataclasses import dataclass
from multiprocessing.connection import Connection
from typing import Any
DISALLOWED_NODES = (
ast.Global,
ast.Nonlocal,
ast.ClassDef,
)
ALLOWED_IMPORTS = {
"collections",
"datetime",
"itertools",
"json",
"math",
"re",
"statistics",
"string",
"textwrap",
"unicodedata",
}
DISALLOWED_NAMES = {
"__import__",
"breakpoint",
"compile",
"delattr",
"dir",
"eval",
"exec",
"exit",
"getattr",
"globals",
"help",
"input",
"locals",
"open",
"quit",
"setattr",
"vars",
}
@dataclass(frozen=True)
class ToolExecutionResult:
ok: bool
stdout: str = ""
result: str | None = None
error: str | None = None
def to_json(self, output_limit: int) -> str:
obj = {
"ok": self.ok,
"stdout": self.stdout[:output_limit],
"result": self.result[:output_limit] if self.result is not None else None,
"error": self.error[:output_limit] if self.error is not None else None,
}
return json.dumps(obj, ensure_ascii=False)
class _BoundedTextBuffer:
"""A minimal text stream that never retains more than ``limit`` chars."""
def __init__(self, limit: int) -> None:
self.limit = max(0, limit)
self._parts: list[str] = []
self._length = 0
def write(self, value: str) -> int:
text = str(value)
remaining = self.limit - self._length
if remaining > 0:
chunk = text[:remaining]
self._parts.append(chunk)
self._length += len(chunk)
return len(text)
def flush(self) -> None:
return None
def getvalue(self) -> str:
return "".join(self._parts)
def _bounded_repr(value: Any, output_limit: int) -> str:
formatter = reprlib.Repr()
formatter.maxlevel = 4
formatter.maxdict = 20
formatter.maxlist = 20
formatter.maxtuple = 20
formatter.maxset = 20
formatter.maxfrozenset = 20
formatter.maxdeque = 20
formatter.maxarray = 20
formatter.maxstring = output_limit
formatter.maxlong = output_limit
formatter.maxother = output_limit
return formatter.repr(value)[:output_limit]
def _configure_worker(memory_limit_mb: int) -> None:
"""Install worker-only signal and address-space limits."""
signal.signal(signal.SIGINT, signal.SIG_DFL)
signal.signal(signal.SIGTERM, signal.SIG_DFL)
import resource
memory_limit_bytes = memory_limit_mb * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (memory_limit_bytes, memory_limit_bytes))
def _send_worker_result(connection: Connection, result: ToolExecutionResult, output_limit: int) -> None:
try:
connection.send(
{
"ok": result.ok,
"stdout": result.stdout[:output_limit],
"result": result.result[:output_limit] if result.result is not None else None,
"error": result.error[:output_limit] if result.error is not None else None,
}
)
except (BrokenPipeError, EOFError, OSError):
pass
finally:
connection.close()
def strip_code_fence(code: str) -> str:
code = (code or "").strip()
code = re.sub(r"^\s*```(?:python)?\s*", "", code, flags=re.IGNORECASE)
code = re.sub(r"\s*```\s*$", "", code)
return code.strip()
def validate_code(code: str) -> None:
tree = ast.parse(code, mode="exec")
for node in ast.walk(tree):
if isinstance(node, DISALLOWED_NODES):
raise ValueError(f"Disallowed Python syntax: {node.__class__.__name__}")
if isinstance(node, ast.Import):
for alias in node.names:
root = alias.name.split(".", 1)[0]
if alias.name not in ALLOWED_IMPORTS or root not in ALLOWED_IMPORTS:
raise ValueError(f"Disallowed import: {alias.name}")
if alias.asname and (alias.asname in DISALLOWED_NAMES or alias.asname.startswith("__")):
raise ValueError(f"Disallowed import alias: {alias.asname}")
if isinstance(node, ast.ImportFrom):
module = node.module or ""
if node.level != 0 or module not in ALLOWED_IMPORTS:
raise ValueError(f"Disallowed import: {module}")
for alias in node.names:
if alias.name == "*" or alias.name.startswith("__"):
raise ValueError(f"Disallowed import name: {alias.name}")
if alias.asname and (alias.asname in DISALLOWED_NAMES or alias.asname.startswith("__")):
raise ValueError(f"Disallowed import alias: {alias.asname}")
if isinstance(node, ast.Name):
if node.id in DISALLOWED_NAMES or node.id.startswith("__"):
raise ValueError(f"Disallowed name: {node.id}")
if isinstance(node, ast.Attribute) and node.attr.startswith("__"):
raise ValueError(f"Disallowed attribute: {node.attr}")
def _safe_worker(
code: str,
context: dict[str, Any],
connection: Connection,
output_limit: int,
memory_limit_mb: int,
) -> None:
import collections
import itertools
import math
import re as re_module
import statistics
try:
_configure_worker(memory_limit_mb)
except Exception:
_send_worker_result(
connection,
ToolExecutionResult(
ok=False,
error="failed to configure Python tool process limits:\n" + traceback.format_exc(limit=4),
),
output_limit,
)
return
safe_modules = {
"collections": collections,
"itertools": itertools,
"json": json,
"math": math,
"re": re_module,
"statistics": statistics,
}
def restricted_import(name, globals=None, locals=None, fromlist=(), level=0):
if level != 0:
raise ImportError("relative imports are not allowed")
if name not in safe_modules:
raise ImportError(f"import of {name!r} is not allowed")
return safe_modules[name]
safe_builtins = {
"abs": abs,
"all": all,
"any": any,
"bool": bool,
"dict": dict,
"enumerate": enumerate,
"filter": filter,
"float": float,
"int": int,
"isinstance": isinstance,
"len": len,
"list": list,
"map": map,
"max": max,
"min": min,
"print": print,
"range": range,
"repr": repr,
"reversed": reversed,
"round": round,
"set": set,
"sorted": sorted,
"str": str,
"sum": sum,
"tuple": tuple,
"zip": zip,
"__import__": restricted_import,
}
namespace = {
"__builtins__": safe_builtins,
"collections": collections,
"itertools": itertools,
"json": json,
"math": math,
"re": re_module,
"statistics": statistics,
}
namespace.update(context)
stdout = _BoundedTextBuffer(output_limit)
try:
validate_code(code)
compiled = compile(code, "<qwen_agent_python_tool>", "exec")
with contextlib.redirect_stdout(stdout):
exec(compiled, namespace, namespace)
result = None
if "result" in namespace:
result = _bounded_repr(namespace["result"], output_limit)
payload = ToolExecutionResult(ok=True, stdout=stdout.getvalue(), result=result)
except MemoryError:
payload = ToolExecutionResult(
ok=False,
stdout=stdout.getvalue(),
error=f"memory limit exceeded ({memory_limit_mb} MiB)",
)
except Exception:
payload = ToolExecutionResult(
ok=False,
stdout=stdout.getvalue(),
error=traceback.format_exc(limit=4),
)
_send_worker_result(connection, payload, output_limit)
def _terminate_process(process: mp.Process, grace_seconds: float) -> str:
"""Stop and reap a worker, escalating from SIGTERM to SIGKILL."""
process.terminate()
process.join(grace_seconds)
if not process.is_alive():
return "SIGTERM"
process.kill()
process.join(grace_seconds)
if process.is_alive():
raise RuntimeError(f"failed to reap Python tool process pid={process.pid} after SIGKILL")
return "SIGKILL"
class RestrictedPythonExecutor:
def __init__(
self,
*,
timeout_seconds: float = 3.0,
output_limit: int = 4000,
memory_limit_mb: int = 512,
termination_grace_seconds: float = 1.0,
) -> None:
if timeout_seconds <= 0:
raise ValueError("timeout_seconds must be positive")
if output_limit <= 0:
raise ValueError("output_limit must be positive")
if type(memory_limit_mb) is not int or memory_limit_mb <= 0:
raise ValueError("memory_limit_mb must be a positive integer")
if termination_grace_seconds <= 0:
raise ValueError("termination_grace_seconds must be positive")
self.timeout_seconds = timeout_seconds
self.output_limit = output_limit
self.memory_limit_mb = memory_limit_mb
self.termination_grace_seconds = termination_grace_seconds
def run(self, code: str, context: dict[str, Any]) -> str:
code = strip_code_fence(code)
if not code:
return ToolExecutionResult(ok=False, error="empty code").to_json(self.output_limit)
if len(code) > 12000:
return ToolExecutionResult(ok=False, error="code is too long").to_json(self.output_limit)
ctx = mp.get_context("spawn")
receive_connection, send_connection = ctx.Pipe(duplex=False)
process = ctx.Process(
target=_safe_worker,
args=(code, context, send_connection, self.output_limit, self.memory_limit_mb),
)
try:
process.start()
send_connection.close()
process.join(self.timeout_seconds)
if process.is_alive():
signal_used = _terminate_process(process, self.termination_grace_seconds)
return ToolExecutionResult(
ok=False,
error=f"execution timed out after {self.timeout_seconds}s; worker reaped with {signal_used}",
).to_json(self.output_limit)
if receive_connection.poll(0.1):
data = receive_connection.recv()
return ToolExecutionResult(**data).to_json(self.output_limit)
if process.exitcode is not None and process.exitcode < 0:
signal_name = signal.Signals(-process.exitcode).name
return ToolExecutionResult(
ok=False,
error=f"execution terminated by signal {signal_name}",
).to_json(self.output_limit)
return ToolExecutionResult(
ok=False,
error=f"execution failed without returning a result (exitcode={process.exitcode})",
).to_json(self.output_limit)
finally:
send_connection.close()
receive_connection.close()
if process.is_alive():
_terminate_process(process, self.termination_grace_seconds)
if process.exitcode is not None:
process.close()
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