---
title: "LLM-Adapters vs awesome-llms-fine-tuning"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/agi-edgerunners-llm-adapters-vs-curated-awesome-lists-awesome-llms-fine-tuning"
tools: ["agi-edgerunners-llm-adapters", "curated-awesome-lists-awesome-llms-fine-tuning"]
---

# LLM-Adapters vs awesome-llms-fine-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 awesome-llms-fine-tuning if a curated list for LLM fine-tuning resources including tutorials, papers, and tools.

[LLM-Adapters](https://arxiv.org/abs/2304.01933) reports 1.2k GitHub stars, 115 forks, and 55 open issues, last pushed Mar 10, 2024. [awesome-llms-fine-tuning](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning) has 525 stars, 79 forks, and 10 open issues, last pushed Dec 2, 2024. Figures are from public GitHub metadata via [LLM-Adapters's repository](https://github.com/AGI-Edgerunners/LLM-Adapters) and [awesome-llms-fine-tuning's repository](https://github.com/Curated-Awesome-Lists/awesome-llms-fine-tuning).

| | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) |
| --- | --- | --- |
| Tagline | Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs | A comprehensive collection of resources for fine-tuning Large Language Models. |
| Stars | 1,233 | 525 |
| Forks | 115 | 79 |
| Open issues | 55 | 10 |
| Language | Python | - |
| Adopt for | LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. | A curated list for LLM fine-tuning resources including tutorials, papers, and tools. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | (unknown) - (unknown) |
| 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) | [awesome-llms-fine-tuning](/tools/curated-awesome-lists-awesome-llms-fine-tuning.md) |
| --- | --- | --- |
| Days since push | 896d | 629d |
| Open issues (now) | 55 | 10 |
| Stars delta | -1 (30d) | 0 (30d) |
| Open issues delta | 0 (30d) | +1 (30d) |
| Full report | [trust report](/tools/agi-edgerunners-llm-adapters/trust.md) | [trust report](/tools/curated-awesome-lists-awesome-llms-fine-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: awesome-llms-fine-tuning

- **Adopt for:** A curated list for LLM fine-tuning resources including tutorials, papers, and tools.
- **License detail:** (unknown) - (unknown)

## Choose when

### Choose LLM-Adapters if…

- Tags unique to LLM-Adapters: adapters, 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 525) - visibility, not fit.

### Choose awesome-llms-fine-tuning if…

- Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, gpt.
- Need extensive guidance on LLM-specific fine-tuning strategies
- More recently updated (last pushed Dec 2, 2024).

## 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 awesome-llms-fine-tuning

- Looking for real-time interactive support or direct code implementation help
- Favor more specialized tools for immediate performance optimization over broad learning

## Common questions

### What is the difference between LLM-Adapters and awesome-llms-fine-tuning?

LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. awesome-llms-fine-tuning: A comprehensive collection of resources for fine-tuning Large Language Models.. See the comparison table for live GitHub stats and shared categories.

### When should I choose LLM-Adapters over awesome-llms-fine-tuning?

Choose LLM-Adapters over awesome-llms-fine-tuning when Tags unique to LLM-Adapters: adapters, 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 525) - visibility, not fit.

### When should I choose awesome-llms-fine-tuning over LLM-Adapters?

Choose awesome-llms-fine-tuning over LLM-Adapters when Tags unique to awesome-llms-fine-tuning: ai, awesome-list, deep-learning, gpt; Need extensive guidance on LLM-specific fine-tuning strategies; More recently updated (last pushed Dec 2, 2024).

### 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 awesome-llms-fine-tuning?

Looking for real-time interactive support or direct code implementation help Favor more specialized tools for immediate performance optimization over broad learning

### Is LLM-Adapters or awesome-llms-fine-tuning more popular on GitHub?

LLM-Adapters has more GitHub stars (1,233 vs 525). Stars measure visibility, not whether either tool fits your constraints.

### Are LLM-Adapters and awesome-llms-fine-tuning open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to LLM-Adapters or awesome-llms-fine-tuning?

GraphCanon lists graph-backed alternatives at [LLM-Adapters alternatives](/tools/agi-edgerunners-llm-adapters/alternatives) and [awesome-llms-fine-tuning alternatives](/tools/curated-awesome-lists-awesome-llms-fine-tuning/alternatives) ([LLM-Adapters markdown twin](/tools/agi-edgerunners-llm-adapters/alternatives.md), [awesome-llms-fine-tuning markdown twin](/tools/curated-awesome-lists-awesome-llms-fine-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-curated-awesome-lists-awesome-llms-fine-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 awesome-llms-fine-tuning?

LLM-Adapters: Dormant. awesome-llms-fine-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 awesome-llms-fine-tuning?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-Adapters trust report](/tools/agi-edgerunners-llm-adapters/trust); [awesome-llms-fine-tuning trust report](/tools/curated-awesome-lists-awesome-llms-fine-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/_