fine-tuning-with-trl Skill
TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF. Published by NousResearch in hermes-agent.
What is fine-tuning-with-trl Skill?
TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF. Published by NousResearch in hermes-agent. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Data analysis use cases
Technical details
- Install or run with Copy skill directory
When to use fine-tuning-with-trl Skill
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: NousResearch
- Repository: NousResearch/hermes-agent
- Skill file: optional-skills/mlops/training/trl-fine-tuning/SKILL.md
What it does
TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.
Skill instructions
TRL - Transformer Reinforcement Learning Quick start TRL provides post-training methods for aligning language models with human preferences. Installation: bash pip install trl transformers datasets peft accelerate Supervised Fine-Tuning (instruction tuning): python from trl import SFTTrainer trainer = SFTTrainer( model="Qwen/Qwen2.5-0.5B", traindataset=dataset, Prompt-completion pairs ) trainer.train() DPO (align with preferences): python from trl import DPOTrainer, DPOConfig config = DPOConfig(outputdir="model-dpo", beta=0.1) trainer = DPOTrainer( model=model, args=config, traindataset=preferencedataset, chosen/rejected pairs processingclass=tokenizer ) trainer.train() Common workflows Workflow 1: Full RLHF pipeline (SFT → Reward Model → PPO) Complete pipeline from base model to human-aligned model. Copy this checklist: RLHF Training: - [ ] Step 1: Supervised fine-tuning (SFT) - [ ] Step 2: Train reward model - [ ] Step 3: PPO reinforcement learning - [ ] Step 4: Evaluate aligned mode
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Frequently asked questions
What is fine-tuning-with-trl?
fine-tuning-with-trl is a open-source AI agent skill with Copy skill directory. TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.
Who is fine-tuning-with-trl best for?
fine-tuning-with-trl is best for reusing agent instructions, scripts, and references, data analysis workflows.
How do I install fine-tuning-with-trl?
Install or run fine-tuning-with-trl using Copy skill directory. Check fine-tuning-with-trl for the latest setup command.
Is fine-tuning-with-trl actively maintained?
fine-tuning-with-trl may need a closer maintenance check before production use.
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