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Update README.md
Add Supported Trainers section - Axolotl and Example Trainer subsections
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README.md

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@@ -211,6 +211,29 @@ Environments come with detailed logging and reporting support, runs track comple
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# Trainer Integrations
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## Axolotl
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<a href="https://github.com/axolotl-ai-cloud/plugin-atropos">
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<img
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src="https://github.com/user-attachments/assets/be629253-a8b1-4354-b6da-5e404e9c854d"
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alt="Atropos plugin logo"
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width="50%">
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</a>
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Axolotl is a powerful tool for fine-tuning a wide range of AI models, supporting techniques like LoRA and QLoRA through simple YAML configurations.
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The [Atropos plugin for Axolotl](https://github.com/axolotl-ai-cloud/plugin-atropos) seamlessly integrates Atropos' RL environments into Axolotl's training pipelines.
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This allows you to leverage Atropos for reinforcement learning while utilizing Axolotl's extensive features for model fine-tuning.
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To use, follow the readme on the [plugin repository](https://github.com/axolotl-ai-cloud/plugin-atropos).
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## Atropos' Example Trainer
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Atropos repo contains an example trainer that should primarily be used as a reference example to show how a trainer and inference provider can be integrated with Atropos to complete the RL Training Loop.
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To use the example trainer, see this page: [training example guide](example_trainer/README.md)
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## Testing and Debugging Tools
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The trajectory-handler provides several debugging tools to help environment developers test and understand their environments locally without requiring the full distributed infrastructure.

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