Peft — Fine-tune large LLMs with LoRA on limited GPU memory
Peft — Fine-tune large LLMs with LoRA on limited GPU الذاكرة
Start with meaning, then move to detail.
This lesson explains Peft — Fine-tune large LLMs with LoRA on limited GPU memory as part of getting started with Hermes correctly. You will learn what it does, when it matters, and the smallest safe test that proves it works.
If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?
For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.
For advanced readers, inspect Skill metadata, Reference: full SKILL.md, When to use PEFT, then verify failure modes and version compatibility.
No prior experience is required; follow the steps on a safe test setup first.
A clear outcome before you read.
- Understand Peft — Fine-tune large LLMs with LoRA on limited GPU memory without assumed prior knowledge.
- Separate the source description from what still needs testing in your environment.
- Read the first command and identify its inputs and outputs before copying it.
Short definitions before the details.
- Session & memory
- A session holds conversation context, while memory keeps selected facts that should persist.
- Skill
- An instruction bundle that teaches Hermes a repeatable workflow without necessarily adding an external service.
Fine-tune large LLMs with LoRA on limited GPU memory
What does the source say, and in what order?
- 01Skill metadata
Start here to understand the core idea or structure.
- 02Reference: full SKILL.md
Read this after the foundation, then connect it to the previous step.
- 03When to use PEFT
Read this after the foundation, then connect it to the previous step.
- 04Quick start
Read this after the foundation, then connect it to the previous step.
- 05Installation
Read this after the foundation, then connect it to the previous step.
- 06LoRA fine-tuning (standard)
Read this after the foundation, then connect it to the previous step.
- 07QLoRA fine-tuning (memory-efficient)
Read this after the foundation, then connect it to the previous step.
- 08LoRA parameter selection
Read this after the foundation, then connect it to the previous step.
- 09Rank (r) - capacity vs efficiency
Read this after the foundation, then connect it to the previous step.
- 10Alpha (loraalpha) - scaling factor
Finish here to verify the result and special cases.
Copy only after you understand the effect.
# Basic installation
pip install peft
# With quantization support (recommended)
pip install peft bitsandbytes
# Full stack
pip install peft transformers accelerate bitsandbytes datasets### QLoRA fine-tuning (memory-efficient)## LoRA parameter selection
### Rank (r) - capacity vs efficiency
| Rank | Trainable Params | Memory | Quality | Use Case |
|------|-----------------|--------|---------|----------|
| 4 | ~3M | Minimal | Lower | Simple tasks, prototyping |
| **8** | ~7M | Low | Good | **Recommended starting point** |
| **16** | ~14M | Medium | Better | **General fine-tuning** |
| 32 | ~27M | Higher | High | Complex tasks |
| 64 | ~54M | High | Highest | Domain adaptation, 70B models |
### Alpha (lora_alpha) - scaling factorRead the first command and identify its inputs and outputs before copying it.
Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.