---
title: "LLM-Adapters vs LLM-RLHF-Tuning"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/agi-edgerunners-llm-adapters-vs-joyce94-llm-rlhf-tuning"
tools: ["agi-edgerunners-llm-adapters", "joyce94-llm-rlhf-tuning"]
---

# LLM-Adapters vs LLM-RLHF-Tuning

*GraphCanon updated Aug 24, 2026*

## Verdict

Pick LLM-Adapters if lLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing; pick LLM-RLHF-Tuning if framework for tuning large language models with PEFT & LoRA techniques like SFT, RM, PPO, DPO.

[LLM-Adapters](https://arxiv.org/abs/2304.01933) reports 1.2k GitHub stars, 115 forks, and 55 open issues, last pushed Mar 10, 2024. [LLM-RLHF-Tuning](https://github.com/Joyce94/LLM-RLHF-Tuning) has 452 stars, 24 forks, and 3 open issues, last pushed Oct 11, 2023. Figures are from public GitHub metadata via [LLM-Adapters's repository](https://github.com/AGI-Edgerunners/LLM-Adapters) and [LLM-RLHF-Tuning's repository](https://github.com/Joyce94/LLM-RLHF-Tuning).

| | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [LLM-RLHF-Tuning](/tools/joyce94-llm-rlhf-tuning.md) |
| --- | --- | --- |
| Tagline | Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs | LLM Tuning with PEFT (SFT+RM+PPO+DPO with LoRA) |
| Stars | 1,233 | 452 |
| Forks | 115 | 24 |
| Open issues | 55 | 3 |
| Language | Python | Python |
| Adopt for | LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. | Framework for tuning large language models with PEFT & LoRA techniques like SFT, RM, PPO, DPO. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | - |
| Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [LLM-RLHF-Tuning](/tools/joyce94-llm-rlhf-tuning.md) |
| --- | --- | --- |
| Days since push | 896d | 1048d |
| Open issues (now) | 55 | 3 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/agi-edgerunners-llm-adapters/trust.md) | [trust report](/tools/joyce94-llm-rlhf-tuning/trust.md) |

## Decision facts: LLM-Adapters

- **Adopt for:** LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing.

## Decision facts: LLM-RLHF-Tuning

- **Adopt for:** Framework for tuning large language models with PEFT & LoRA techniques like SFT, RM, PPO, DPO.

## Choose when

### Choose LLM-Adapters if…

- Tags unique to LLM-Adapters: adapters, large language models, parameter-efficient.
- Optimizing resource usage when you need to fine-tune large language models without altering their core parameters
- More GitHub stars (1.2k vs 452) - visibility, not fit.

### Choose LLM-RLHF-Tuning if…

- Tags unique to LLM-RLHF-Tuning: language-model, llama, llm, lora.
- When you need to fine-tune LLMS using PEFT methods such as SFT+RM+PPO+DPO alongside LoRA.
- Leaner open-issue backlog (3).

## When NOT to use LLM-Adapters

- You require a full retraining approach that modifies all model weights, not just adapters
- Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023

## When NOT to use LLM-RLHF-Tuning

- Avoid if your project only requires basic finetuning without the need for advanced techniques like PEFT or LoRA.
- Not suitable if you require a tool that supports other specific fine-tuning methods not covered by this framework.

## Common questions

### What is the difference between LLM-Adapters and LLM-RLHF-Tuning?

LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. LLM-RLHF-Tuning: LLM Tuning with PEFT (SFT+RM+PPO+DPO with LoRA). See the comparison table for live GitHub stats and shared categories.

### When should I choose LLM-Adapters over LLM-RLHF-Tuning?

Choose LLM-Adapters over LLM-RLHF-Tuning when Tags unique to LLM-Adapters: adapters, large language models, parameter-efficient; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters; More GitHub stars (1.2k vs 452) - visibility, not fit.

### When should I choose LLM-RLHF-Tuning over LLM-Adapters?

Choose LLM-RLHF-Tuning over LLM-Adapters when Tags unique to LLM-RLHF-Tuning: language-model, llama, llm, lora; When you need to fine-tune LLMS using PEFT methods such as SFT+RM+PPO+DPO alongside LoRA; Leaner open-issue backlog (3).

### When should I avoid LLM-Adapters?

You require a full retraining approach that modifies all model weights, not just adapters Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023

### When should I avoid LLM-RLHF-Tuning?

Avoid if your project only requires basic finetuning without the need for advanced techniques like PEFT or LoRA. Not suitable if you require a tool that supports other specific fine-tuning methods not covered by this framework.

### Is LLM-Adapters or LLM-RLHF-Tuning more popular on GitHub?

LLM-Adapters has more GitHub stars (1,233 vs 452). Stars measure visibility, not whether either tool fits your constraints.

### Are LLM-Adapters and LLM-RLHF-Tuning open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to LLM-Adapters or LLM-RLHF-Tuning?

GraphCanon lists graph-backed alternatives at [LLM-Adapters alternatives](/tools/agi-edgerunners-llm-adapters/alternatives) and [LLM-RLHF-Tuning alternatives](/tools/joyce94-llm-rlhf-tuning/alternatives) ([LLM-Adapters markdown twin](/tools/agi-edgerunners-llm-adapters/alternatives.md), [LLM-RLHF-Tuning markdown twin](/tools/joyce94-llm-rlhf-tuning/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/agi-edgerunners-llm-adapters-vs-joyce94-llm-rlhf-tuning.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, LLM-Adapters or LLM-RLHF-Tuning?

LLM-Adapters: Dormant. LLM-RLHF-Tuning: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for LLM-Adapters and LLM-RLHF-Tuning?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-Adapters trust report](/tools/agi-edgerunners-llm-adapters/trust); [LLM-RLHF-Tuning trust report](/tools/joyce94-llm-rlhf-tuning/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters`](/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_