Skip to main content

ReinforceNow CLI - Reinforcement Learning platform command-line interface

Project description

ReinforceNow CLI

PyPI version Docs Follow on X MIT License

Documentation

See the documentation for a technical overview of the platform and train your first agent

Quick Start

1. Install uv (Python package manager)

# macOS/Linux:
$ curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows:
PS> powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Install ReinforceNow

uv init && uv venv --python 3.11
source .venv/bin/activate  # Windows: .\.venv\Scripts\Activate.ps1
uv pip install rnow

3. Authenticate

rnow login

4. Create & Run Your First Project

rnow init --template sft
rnow run

That's it! Your training run will start on ReinforceNow's infrastructure. Monitor progress in the dashboard.

ReinforceNow Graph

Core Concepts

Go from raw data to a reliable AI agent in production. ReinforceNow gives you the flexibility to define:

1. Reward Functions

Define how your model should be evaluated using the @reward decorator:

from rnow.core import reward, RewardArgs

@reward
async def accuracy(args: RewardArgs, messages: list) -> float:
    """Check if the model's answer matches ground truth."""
    response = messages[-1]["content"]
    expected = args.metadata["answer"]
    return 1.0 if expected in response else 0.0

Write your first reward function

2. Tools (for Agents)

Give your model the ability to call functions during training:

from rnow.core import tool

@tool
def search(query: str, max_results: int = 5) -> dict:
    """Search the web for information."""
    # Your implementation here
    return {"results": [...]}

Train an agent with custom tools

3. Training Data

Create a train.jsonl file with your prompts and reward assignments:

{"messages": [{"role": "user", "content": "Balance the equation: Fe + O2 → Fe2O3"}], "rewards": ["accuracy"], "metadata": {"answer": "4Fe + 3O2 → 2Fe2O3"}}
{"messages": [{"role": "user", "content": "Balance the equation: H2 + O2 → H2O"}], "rewards": ["accuracy"], "metadata": {"answer": "2H2 + O2 → 2H2O"}}
{"messages": [{"role": "user", "content": "Balance the equation: N2 + H2 → NH3"}], "rewards": ["accuracy"], "metadata": {"answer": "N2 + 3H2 → 2NH3"}}

Learn about training data format

Contributing

We welcome contributions! ❤️ Please open an issue to discuss your ideas before submitting a PR


ReinforceNow

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

rnow-0.3.6.tar.gz (1.1 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

rnow-0.3.6-py3-none-any.whl (1.1 MB view details)

Uploaded Python 3

File details

Details for the file rnow-0.3.6.tar.gz.

File metadata

  • Download URL: rnow-0.3.6.tar.gz
  • Upload date:
  • Size: 1.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for rnow-0.3.6.tar.gz
Algorithm Hash digest
SHA256 626bbcca4cb8bb7896996ae5172eaa59feef4c3e8d6c8d66a6214dd3590fff7e
MD5 4a7f5ea20bbbf2c8d92f386d5bdc69fd
BLAKE2b-256 9f0d63ff28565bf21d75482a4738adbc00b457c11d02dce08eb812bb8b71f9de

See more details on using hashes here.

File details

Details for the file rnow-0.3.6-py3-none-any.whl.

File metadata

  • Download URL: rnow-0.3.6-py3-none-any.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for rnow-0.3.6-py3-none-any.whl
Algorithm Hash digest
SHA256 320452f249b0542810cede04fd8372572e040c6b628e727175369e1fbb1dff65
MD5 98276dc397454ca535a6396dd1e98a6f
BLAKE2b-256 5b08a77ae524f2075102f41e6ac2f1fe7c747c0389af132fb638eb2dd1203270

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page