slime Skill
RL post-training for LLMs with Megatron and SGLang. Published by NousResearch in hermes-agent.
Decision snapshot
Is this a fit?
Data analysis, Research, Includes SKILL.md, Reusable instructions
Compatibility not yet detected.
Permission behavior not yet detected.
Copy skill directory
30 days ago · MIT license
No specific cautions were detected. Review the source and requested permissions before installing.
What is slime Skill?
RL post-training for LLMs with Megatron and SGLang. 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
- Research
- Data analysis use cases
- Research use cases
Declared skill metadata
- Declared author: Orchestra Research
- Declared license: MIT
- Source file: optional-skills/mlops/slime/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 slime Skill
- Use it for data analysis.
- Use it for research.
Built with
Editorial notes
Source
- Creator: NousResearch
- Repository: NousResearch/hermes-agent
- Skill file: optional-skills/mlops/slime/SKILL.md
What it does
RL post-training for LLMs with Megatron and SGLang.
Skill instructions
slime: LLM Post-Training Framework for RL Scaling slime is an LLM post-training framework from Tsinghua's THUDM team, powering GLM-4.5, GLM-4.6, and GLM-4.7. It connects Megatron-LM for training with SGLang for high-throughput rollout generation. When to Use slime Choose slime when you need: - Megatron-LM native training with SGLang inference - Custom data generation workflows with flexible data buffers - Training GLM, Qwen3, DeepSeek V3, or Llama 3 models - Research-grade framework with production backing (Z.ai) Consider alternatives when: - You need enterprise-grade stability features → use miles - You want flexible backend swapping → use verl - You need PyTorch-native abstractions → use torchforge Key Features - Training: Megatron-LM with full parallelism support (TP, PP, DP, SP) - Rollout: SGLang-based high-throughput generation with router - Data Buffer: Flexible prompt management and sample storage - Models: GLM-4.x, Qwen3, DeepSeek V3/R1, Llama 3 Architecture Overview ┌─────────
Verified compatibility and discovery
Frequently asked questions
What is slime?
slime is a open-source AI agent skill with Copy skill directory. RL post-training for LLMs with Megatron and SGLang.
Who is slime best for?
slime is best for reusing agent instructions, scripts, and references, data analysis workflows, research workflows.
How do I install slime?
Install or run slime using Copy skill directory. Check slime for the latest setup command.
Is slime actively maintained?
slime may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
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.
