google-gemini
Challenges in Running a Fine-Tuned Gemini Model with Multi-Agent Frameworks
Description of the feature request:
Improved Support for Running Fine-Tuned Gemini Models with Multi-Agent Frameworks
Currently, there is no clear and definitive way to run a fine-tuned Gemini model from Google AI Studio using multi-agent frameworks. Despite exploring the available documentation, I found it to be insufficient and ambiguous, making it challenging to integrate the model effectively.
Requested Improvements:
Comprehensive Documentation: Detailed and structured documentation with clear steps for deploying fine-tuned Gemini models in multi-agent environments.
Example Implementations: Sample code and tutorials demonstrating how to run Gemini models with frameworks like LangChain, AutoGen, or custom multi-agent setups.
API Enhancements & Clarity: More explicit API references, including supported input/output formats and potential limitations when using multi-agent configurations.
Deployment Guides: Best practices for running fine-tuned models on different platforms (local, cloud, and edge environments).
Debugging & Troubleshooting Support: Common issues, logs interpretation, and troubleshooting guidelines for smooth integration.
What problem are you trying to solve with this feature?
Running a fine-tuned Gemini model from Google AI Studio within multi-agent frameworks lacks clear, structured guidance. The existing documentation is insufficient and ambiguous, making it difficult to:
Integrate with Multi-Agent Frameworks – There are no definitive steps or examples for using Gemini models with frameworks like LangChain, CrewAI, or other custom multi-agent setups.
Deploy Fine-Tuned Models Efficiently – Unclear API references and missing best practices make it challenging to deploy fine-tuned models on different platforms (local, cloud, or edge).
Understand Input/Output Handling – Lack of detailed specifications on how the model handles prompts, responses, and multi-agent interactions.
Debug & Troubleshoot Issues – Limited guidance on handling common errors, API constraints, or performance optimization when running fine-tuned Gemini models in a multi-agent setting.
Any other information you'd like to share?
Frameworks Considered: I have explored multi-agent frameworks like CrewAI, LangChain, and custom orchestration setups, but there is no clear guidance on how to integrate fine-tuned Gemini models with them.
Challenges Faced:
Lack of direct API support or examples for multi-agent interaction.
Unclear documentation on model deployment and inference workflows.
Difficulty in handling context persistence across multiple agents.
Expected Outcome: A well-defined process for using Gemini models in multi-agent environments, including:
Step-by-step setup guides.
Example scripts for popular multi-agent frameworks.
Clear API specifications with explanations of input/output handling.
Potential Workarounds: I’ve considered using external wrappers or intermediary APIs, but these solutions feel inefficient and require unnecessary overhead.
