trl-fine-tuning Skill
TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF. Published by NousResearch in hermes-agent.
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Data analysis, Includes SKILL.md, Reusable instructions
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30 days ago · MIT license
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What is trl-fine-tuning Skill?
TRL: SFT, DPO, GRPO, RLOO 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. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Data analysis use cases
Declared skill metadata
- Declared author: Orchestra Research
- Declared license: MIT
- Source file: optional-skills/mlops/training/trl-fine-tuning/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use trl-fine-tuning 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, GRPO, RLOO 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 → RLOO) Complete pipeline from base model to human-aligned model. Note (TRL 1.x): PPO has been removed from TRL — PPOTrainer, PPOConfig, and python -m trl.scripts.ppo no longer exist. Use an online-RL trainer TRL still ships: RLOO (RLOOTrainer / trl rlo
Verified compatibility and discovery
Frequently asked questions
What is trl-fine-tuning?
trl-fine-tuning is a open-source AI agent skill with Copy skill directory. TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF.
Who is trl-fine-tuning best for?
trl-fine-tuning is best for reusing agent instructions, scripts, and references, data analysis workflows.
How do I install trl-fine-tuning?
Install or run trl-fine-tuning using Copy skill directory. Check trl-fine-tuning for the latest setup command.
Is trl-fine-tuning actively maintained?
trl-fine-tuning may need a closer maintenance check before production use.
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Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
