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LlamaGym

LlamaGym

Agent framework by LlamaGym

Transform your AI development with LlamaGym, the open-source Python framework that simplifies fine-tuning large language model agents through intuitive online reinforcement learning.

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github.com/KhoomeiK/LlamaGym

LlamaGym is an innovative, open-source Python framework designed to streamline the fine-tuning of large language model (LLM) agents through online reinforcement learning. Developed by KhoomeiK, LlamaGym simplifies the complexities associated with training LLM-based agents by managing essential tasks such as conversation context, episode batching, reward assignment, and proximal policy optimization (PPO) setup. This makes it a valuable resource for developers and researchers interested in advancing their work in AI.

Features

LlamaGym offers a range of features that facilitate the fine-tuning of LLM agents, making it easier for developers to focus on their models without getting bogged down by the underlying complexities. Below is a summary of the key features:

Feature Description
Simplified Fine-Tuning Process Provides a standardized environment similar to OpenAI's Gym, allowing developers to focus on model training and abstracting away complexities.
Easy Integration Highly customizable and easy to integrate into existing projects, enabling rapid experimentation with agent prompting and hyperparameters.
Customizable Abstract Agent class allows for customization according to specific needs in AI applications, making it suitable for both research and development.

Use Cases

LlamaGym can be applied in various scenarios, showcasing its versatility and capability to enhance AI applications:

  • Fine-Tuning LLM Agents: It is primarily used for fine-tuning LLM agents to perform tasks in various environments, improving the responsiveness and capability of AI agents through online reinforcement learning.
  • Research and Development: Researchers and developers can leverage LlamaGym for rapid experimentation with different hyperparameters and agent prompts, accelerating the development process in AI.
  • AI Applications: From chatbots to complex decision-making systems, LlamaGym is applicable in a wide range of AI applications, making it an essential tool for anyone working with large language models.

How to get started

To get started with LlamaGym, you need to install it using pip. Here’s the command to install the framework:

pip install llamagym

After installation, you can create your own Agent class by defining the following three abstract methods: get_system_prompt, format_observation, and extract_action. Below is an example implementation for a Blackjack agent:

from llamagym import Agent

class BlackjackAgent(Agent):
    def get_system_prompt(self) -> str:
        return "You are an expert blackjack player."

    def format_observation(self, observation) -> str:
        return f"Your current total is {observation[0]}"

    def extract_action(self, response: str):
        return 0 if "stay" in response else 1

model = AutoModelForCausalLMWithValueHead.from_pretrained("Llama-2-7b").to(device)
tokenizer = AutoTokenizer.from_pretrained("Llama-2-7b")
agent = BlackjackAgent(model, tokenizer, device)

LlamaGym is currently a weekend project and is still evolving, but it has already demonstrated significant potential in simplifying the fine-tuning process of LLM agents. Contributions to the project are welcome, and ongoing community involvement is expected to enhance its capabilities further.

Pricing Information for LlamaGen.ai

The pricing for LlamaGen.ai is structured around various plans and additional credit packages:

  • Free Plan: 1000 initial credits
  • Standard Plan: 15,000 credits per month
  • Pro Plan: 45,000 credits per month
  • Unlimited Plan: 135,000 credits per month

Additional Credit Packages

  • 1500 credits
  • 3000 credits
  • 7500 credits

Note: Pricing is an indication and may vary over time.