here's the link to the repo
↳ https://
github.com/hiyouga/LLaMA-
Factory
…
CODE
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LLaMA-Factory Repository Link Shared on GitHub
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LLaMA-Factory: Zero-Code Fine-Tuning for 100+ LLMs
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Fine tune 100+ open source LLMs with zero code
— Charly Wargnier (@DataChaz) 24 mai 2025
Meet LLaMA-Factory! 🦙🔥
a full-stack open source UI for training LLMs and VLMs (LLaMA, Mistral, Qwen, Yi, DeepSeek, Gemma)
— no boilerplate!
why it matters:
↳ 100+ model support
↳ PPO, DPO, SFT, RLHF
↳ GaLore, DoRA, PiSSA,… pic.twitter.com/orPH4wXFH7Fine tune 100+ open source LLMs with zero code Meet LLaMA-Factory! a full-stack open source UI for training LLMs and VLMs (LLaMA, Mistral, Qwen, Yi, DeepSeek, Gemma)
— no boilerplate! why it matters: ↳ 100+ model support
↳ PPO, DPO, SFT, RLHF
↳ GaLore, DoRA, PiSSA, -
Latent Space Poetry: 22-Page AI-Generated Creative Work
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Read all 22 page of latent space poetry: https://
thevibeofcode.replit.app -
The Vibe of Code: Dr. Seuss Meets ChatGPT Programming
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THE VIBE OF CODE, based on Dr. Seuss, a project by @yoheinakajima in collaboration with ChatGPT:
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Agent Error Handling and Guardrail Method Retry Mechanism
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This time, we get the output shown in the image. The lines in yellow indicate the error message received from the guardrail method. The Agent retries a few times to fix it before it exits the program.
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Understanding Failure in Guardrail Methods and Crew Execution
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But now let's understand what happens upon failure. Let's intentionally ask it to generate a 200-word summary instead, keep the guardrail method the same, and kick off the Crew. Here's the code
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Defining ML Summarizer Agent with Guardrail Validation
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Next, we define a summarizer Agent and its Task—asking it to summarize an ML topic. We pass the Guardrail method defined above (validate_summary_length) as a parameter to the Task and kick off the Crew. Here's the code
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Guardrails for Agent Output Validation and Word Limit Enforcement
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Consider an Agent to summarize research topics. The summary must be under 150 words—a Guardrail. A Guardrail accepts the task's output and returns two values: • Success (True) or Failure (False).
• The validated output (success) or an error message (failure). Check this -
Implementing Guardrails for Reliable AI Agent Outputs
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AI agents are powerful, but without safeguards, they can hallucinate, enter infinite loops, or give unreliable outputs.
— Akshay 🚀 (@akshay_pachaar) 24 mai 2025
Guardrails solve this. We can use them to:
• Limit tool usage
• Set validation checkpoints
• Specify fallback mechanisms
Below, let's implement them! pic.twitter.com/QuWzV5z5O4AI agents are powerful, but without safeguards, they can hallucinate, enter infinite loops, or give unreliable outputs. Guardrails solve this. We can use them to:
• Limit tool usage
• Set validation checkpoints
• Specify fallback mechanisms Below, let's implement them! -
Hugging Face Launches MCP Server Registry for Gradio Apps
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You can now filter Spaces for MCP Compatibility We already host 500,000 @Gradio apps, any of which can be turned into an MCP server with a single line change. We're excited to work with the community to create the biggest MCP server registry ever, directly on @huggingface
!