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roanbrasil
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devops-gitops-corpus

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Sample YAML configuration files in the `examples` directory cover:
- LoRA SFT on Llama 3
- DPO on Mistral
- Other training scenarios
Copy a sample config, adapt it to your model and data, set hyperparameters (learning rate, epochs, batch size).
**Web UI method**: Launch the LLaMA Board UI:
```bash
llamafactory-cli webui
```
This launches a Gradio interface for selecting models, datasets, fine-tuning methods, and parameters.
**Test the model**: Chat with your fine-tuned model:
```bash
llamafactory-cli chat path_to_your_finetuned_model_or_adapter_config.yaml
```
### Post-Training
**Export model**: Merge LoRA adapters into the base model:
```bash
llamafactory-cli export your_config.yaml
```
**Hugging Face compatibility**: Exported models are compatible with Hugging Face Hub.
### Documentation Resources
- **Official Documentation**: [llamafactory.readthedocs.io](https://llamafactory.readthedocs.io/en/latest/)
- **Examples Directory**: GitHub repository with scripts and configurations
- **GitHub Issues**: Search existing issues or open new ones
## Summary
LLaMA-Factory is a toolkit for fine-tuning large language and vision-language models. It supports a range of models and training methods, including recent additions like OFT/OFTv2 and support for new model families.
The toolkit reduces boilerplate code for fine-tuning workflows while maintaining flexibility in model selection and training approaches.
## References
- **GitHub Repository**: [hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
- **Documentation**: [llamafactory.readthedocs.io](https://llamafactory.readthedocs.io/en/latest/)
- **Research Paper (ACL 2024)**: [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)
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# source: VoltAgent__voltagent/website/blog/2025-07-02-top-llm-observability/index.md type: docs
---
title: Top 5 LLM Observability Tools
description: The best tools to monitor your LLM applications in production.
slug: llm-observability-tools
image: https://cdn.voltagent.dev/2025-07-02-top-llm-observability/social.png
authors: necatiozmen
---
import ZoomableMermaid from '@site/src/components/blog-widgets/ZoomableMermaid';
## The Real Talk About LLM Monitoring
Let me tell you something - building LLM apps is _fun_. Monitoring them in production? That's where things get... interesting.
You know that feeling when your AI agent works perfectly in development, but then you deploy it and suddenly it's making weird decisions, burning through your API budget, or just... not doing what it's supposed to do? Yeah, I've been there. We've all been there.
Here's what typically happens in production:
<ZoomableMermaid
chart={`
%%{init: {'theme':'base', 'themeVariables': {'primaryColor': '#10b981', 'primaryTextColor': '#10b981', 'primaryBorderColor': '#10b981', 'lineColor': '#10b981', 'secondaryColor': '#ecfdf5', 'tertiaryColor': '#d1fae5', 'background': '#ffffff', 'mainBkg': '#ecfdf5', 'secondBkg': '#d1fae5', 'tertiaryBkg': '#a7f3d0'}}}%%
sequenceDiagram
participant User as User
participant App as Your App
participant LLM as LLM Provider
participant Monitor as ???
participant You as You (Developer)
User->>App: "Help me with this task"
App->>LLM: Complex prompt + tools
LLM->>LLM: Makes decisions you can't see
LLM->>App: Response + tool calls
App->>User: Final response
Note over Monitor: Where's the visibility?
Note over You: Something's wrong...
User->>You: "Your AI is acting weird"
You->>App: Check logs
App->>You: Basic HTTP logs only
You->>You: Start debugging hell 🔥
`}
/>
Sound familiar? That's exactly why I'm writing this guide.
After building AI apps for the past year and going through this pain myself, I've tested pretty much every observability tool out there. Some are great, some are... well, let's just say they try their best.