LoRA and Parameter-Efficient Fine-Tuning

Note: Canonical source of truth for LoRA/PEFT in AI-OS. Other LoRA/PEFT docs in this folder have been consolidated into this page.

Quick links: - Quick start presets: see Configuration Presets - Parameter impact overview: see PEFT Methods Comparison and Target Modules Explained - Troubleshooting and validation: see Testing & Validation

Date: October 19, 2025
System: AI-OS HRM ACTv1 Training
PEFT Library: Hugging Face PEFT (v0.11.1+)

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Table of Contents

1. Overview
2. What is PEFT?
3. Implementation Details
4. Parameter Breakdown
5. PEFT Methods Comparison
6. Target Modules Explained
7. Configuration Presets
8. Memory & Performance Impact
9. Best Practices
10. Testing & Validation
11. Commands (CLI)
12. Inputs & Outputs
13. Try it (PowerShell)

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Overview

Parameter-Efficient Fine-Tuning (PEFT) in AI-OS allows training with 95-99% fewer trainable parameters by using adapter techniques like LoRA. Instead of updating all 87M+ model parameters, PEFT adds small adapter layers (~500K-8M params) that achieve comparable or better results.

Key Benefits: - ✅ Memory Reduction: 40-60% less VRAM usage - ✅ Speed: Faster training and convergence - ✅ Quality: Comparable or better results than full fine-tuning - ✅ Flexibility: Easy to merge adapters or switch between them - ✅ Compatibility: Works with all other optimizations (gradient checkpointing, AMP, etc.)

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What is PEFT?

PEFT techniques modify only a small subset of model parameters while keeping the base model frozen. This is achieved through:

1. Adapter Layers: Small neural network modules inserted into the model
2. Low-Rank Decomposition: Decomposing weight updates into smaller matrices
3. Selective Training: Only training specific components (e.g., attention layers)

Why Use PEFT?

Scenario	Full Fine-Tuning	PEFT (LoRA)
Parameters to train	87M (100%)	500K-8M (1-5%)
VRAM Required (GPT-2 size)	12-16 GB	6-10 GB
Training Speed	Baseline	1.5-2× faster
Convergence	Requires more data	Often better with less data
Risk of Catastrophic Forgetting	High	Low
Storage per fine-tune	Full model (~350 MB)	Adapter only (~10-30 MB)

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Implementation Details

Code Location

• Main Implementation: src/aios/cli/hrm_hf/model_precision.py (apply_peft() function)
• Configuration: src/aios/core/hrm_training/training_config/advanced_fields.py
• GUI Controls: src/aios/gui/components/hrm_training_panel/

How It Works

# From model_precision.py
def apply_peft(model, config, log_fn):
    if not config.use_peft:
        return model

    # 1. Parse target modules
    target_modules_list = [m.strip() for m in config.lora_target_modules.split(',')]

    # 2. Create PEFT config
    if config.peft_method == "lora":
        peft_config = LoraConfig(
            r=config.lora_r,                    # Rank
            lora_alpha=config.lora_alpha,      # Scaling
            lora_dropout=config.lora_dropout,  # Regularization
            target_modules=target_modules_list,
            task_type=TaskType.CAUSAL_LM,
        )
    # ... (adalora, ia3 methods also supported)

    # 3. Wrap model with PEFT
    model = get_peft_model(model, peft_config)

    return model

Integration Points

1. Training Pipeline: Called in train_actv1_impl() after model creation
2. Memory Estimation: Integrated into VRAM calculator in GUI
3. Checkpoint Saving: PEFT adapters saved separately or merged
4. Inference: Can load adapters dynamically

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Parameter Breakdown

1. use_peft (Boolean)

Default: false

Description: Master switch to enable/disable PEFT.

When to Enable: - ✅ Limited VRAM (< 12 GB available) - ✅ Want faster training iteration - ✅ Fine-tuning for specific tasks - ✅ Need to maintain multiple model variants

When to Disable: - ❌ Full model capacity needed - ❌ Training from scratch (not fine-tuning) - ❌ Abundant VRAM available (24+ GB)

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2. peft_method (String)

Default: "lora"
Options: lora, adalora, ia3

LoRA (Low-Rank Adaptation) 🌟 Recommended

• Best for: General purpose, most stable
• Params: Configurable via lora_r
• Quality: Excellent
• Speed: Fast

How it works: Adds low-rank matrices A and B to weight updates

ΔW = B × A (where B is d×r and A is r×k, r << d,k)

AdaLoRA (Adaptive LoRA)

• Best for: Dynamic rank allocation
• Params: Similar to LoRA
• Quality: Potentially better than LoRA
• Speed: Slightly slower (adaptive overhead)

How it works: Dynamically adjusts rank across layers based on importance

IA3 (Infused Adapter)

• Best for: Minimal parameters (~100K)
• Params: Fewest parameters
• Quality: Good for specific tasks
• Speed: Fastest

How it works: Learns scaling vectors instead of full matrices

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3. lora_r (Integer - Rank)

Default: 16
Range: 1-256 (practical: 4-64)

Description: The rank of the low-rank decomposition. Controls adapter capacity.

Impact on Model: - Higher rank = More capacity, more parameters, more VRAM - Lower rank = Less capacity, fewer parameters, less VRAM

Parameter Count Formula:

params_per_layer = 2 × rank × layer_dimension
For GPT-2 (d=768), q_proj with r=16:
  params = 2 × 16 × 768 = 24,576 params per layer

Recommendations:

Rank	Parameters	VRAM Impact	Use Case
r=4	~250K	+1 GB	Very simple fine-tuning
r=8	~500K	+1.5 GB	Minimal configuration
r=16	~2M	+2-3 GB	Recommended default
r=32	~8M	+4-5 GB	Complex tasks, high quality
r=64	~32M	+8-10 GB	Very complex, rarely needed

Rule of Thumb: - Start with r=16 - Increase if model underfits - Decrease if VRAM limited or overfitting occurs

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4. lora_alpha (Integer - Scaling)

Default: 32
Range: 1-1024 (practical: 8-128)

Description: Scaling parameter for LoRA adapter outputs.

Mathematical Impact:

effective_adapter_contribution = (lora_alpha / lora_r) × adapter_output

Effective Learning Rate: - Higher alpha relative to r = Stronger adapter influence - Lower alpha relative to r = More conservative adaptation

Recommendations:

Configuration	Ratio	Use Case
r=8, α=8	1:1	Conservative, minimal changes
r=16, α=32	2:1	Standard (recommended)
r=16, α=16	1:1	More conservative
r=16, α=64	4:1	Aggressive adaptation
r=32, α=64	2:1	High capacity, standard scaling

Best Practice: - Use lora_alpha = 2 × lora_r as starting point - Increase alpha if adapters aren't learning enough - Decrease alpha if training is unstable

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5. lora_dropout (Float)

Default: 0.05
Range: 0.0-0.5 (practical: 0.0-0.2)

Description: Dropout probability applied to LoRA adapter layers for regularization.

Purpose: - Prevent overfitting - Improve generalization - Add noise during training

Recommendations:

Dropout	Regularization	Use Case
0.0	None	Large datasets (>100K samples)
0.05	Light (recommended)	General purpose
0.1	Medium	Medium datasets (10K-100K)
0.2-0.3	High	Small datasets (<10K samples)

When to Adjust: - Increase if model overfits to training data - Decrease if model underfits or dataset is very large - Set to 0 for maximum adapter capacity (stable datasets)

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6. lora_target_modules (String - Comma-separated)

Default: "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj"

Description: Specifies which model layers should have LoRA adapters applied.

Available Modules in HRM ACTv1:

Attention Modules (Recommended)

• q_proj - Query projection (attention keys)
• k_proj - Key projection (attention values)
• v_proj - Value projection (what gets attended to)
• o_proj - Output projection (attention combination)

MLP/Feed-Forward Modules

• gate_proj - Gating mechanism
• up_proj - Upward projection (expand)
• down_proj - Downward projection (compress)

Always Trainable (Cannot be frozen)

• lm_head - Language model output head
• q_head - HRM halting/pondering head

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Target Modules Explained

Preset Configurations

Minimal (Recommended for VRAM < 8 GB)

"q_proj,v_proj"

- Parameters: ~500K-1M - VRAM: +1.5-2 GB - Quality: Good for most tasks - Speed: Fastest training - Best for: Limited hardware, quick iterations

Balanced (Recommended Default)

"q_proj,k_proj,v_proj,o_proj"

- Parameters: ~2M-4M - VRAM: +2.5-4 GB - Quality: Very good - Speed: Fast - Best for: General fine-tuning, balanced quality/speed

Full (Maximum Quality)

"q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj"

- Parameters: ~6M-12M - VRAM: +4-6 GB - Quality: Best possible with PEFT - Speed: Moderate - Best for: Complex tasks, maximum quality needed

Module Impact Analysis

Module	Function	Impact on Performance	Training Cost
q_proj	Query generation	⭐⭐⭐ High - Critical for attention	Low
k_proj	Key generation	⭐⭐ Medium - Important for attention	Low
v_proj	Value generation	⭐⭐⭐ High - What gets attended to	Low
o_proj	Attention output	⭐⭐ Medium - Combines attention	Low
gate_proj	MLP gating	⭐ Low-Medium - Controls information flow	Medium
up_proj	MLP expansion	⭐ Low-Medium - Increases dimensionality	Medium
down_proj	MLP compression	⭐ Low-Medium - Reduces dimensionality	Medium

Key Insight: - Attention modules (q,k,v,o) are most impactful per parameter - MLP modules add capacity but with diminishing returns - Always include q_proj and v_proj at minimum

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PEFT Methods Comparison

Detailed Comparison Table

Feature	LoRA	AdaLoRA	IA3
Trainable Params	0.5M-8M	0.5M-8M	50K-500K
Memory Overhead	+2-4 GB	+2.5-5 GB	+1-2 GB
Training Speed	Fast	Medium	Fastest
Quality	Excellent	Excellent+	Good
Stability	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Complexity	Low	Medium	Low
Recommended For	General use	Research, optimal quality	Extreme efficiency
Hyperparameters	r, alpha, dropout	r, alpha, dropout	None (module-specific)

When to Use Each Method

Use LoRA when:

• ✅ General fine-tuning (recommended default)
• ✅ Want predictable, stable results
• ✅ Well-documented hyperparameters
• ✅ Good community support

Use AdaLoRA when:

• ✅ Want slightly better quality
• ✅ Have heterogeneous layers (some need more capacity)
• ✅ Willing to trade speed for quality
• ✅ Experimenting with optimal configurations

Use IA3 when:

• ✅ Extremely limited VRAM
• ✅ Need fastest possible training
• ✅ Task is relatively simple
• ✅ Every MB of memory counts

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Configuration Presets

Preset 1: Budget (< 8 GB VRAM)

use_peft: true
peft_method: "ia3"
lora_target_modules: "q_proj,v_proj"

- Trainable params: ~100K-200K - VRAM overhead: +1-1.5 GB - Quality: Good - Use case: Lightweight fine-tuning, minimal resources

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Preset 2: Efficient (8-12 GB VRAM) 🌟 Recommended

use_peft: true
peft_method: "lora"
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules: "q_proj,v_proj"

- Trainable params: ~500K-1M - VRAM overhead: +2-2.5 GB - Quality: Very Good - Use case: Most common scenarios, balanced efficiency

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Preset 3: Balanced (12-16 GB VRAM)

use_peft: true
peft_method: "lora"
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules: "q_proj,k_proj,v_proj,o_proj"

- Trainable params: ~2M-3M - VRAM overhead: +3-4 GB - Quality: Excellent - Use case: Standard fine-tuning with ample resources

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Preset 4: High Quality (16-24 GB VRAM)

use_peft: true
peft_method: "lora"
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_modules: "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj"

- Trainable params: ~8M-12M - VRAM overhead: +5-6 GB - Quality: Maximum (with PEFT) - Use case: Complex tasks, maximum quality needed

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Preset 5: Adaptive (Research/Optimization)

use_peft: true
peft_method: "adalora"
lora_r: 16
lora_alpha: 32
lora_dropout: 0.1
lora_target_modules: "q_proj,k_proj,v_proj,o_proj"

- Trainable params: ~2M-3M (dynamic) - VRAM overhead: +3.5-4.5 GB - Quality: Excellent+ - Use case: Research, finding optimal configurations

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Memory & Performance Impact

Memory Breakdown (GPT-2 124M Model Example)

Full Fine-Tuning (no PEFT)

Base model:          ~500 MB
Gradients:          ~500 MB
Optimizer states:   ~2000 MB (Adam)
Activations:        ~8000 MB (batch=8, seq=1024)
-----------------------------------
TOTAL:              ~11 GB

PEFT (LoRA r=16, Balanced)

Base model:          ~500 MB (frozen, can use 8-bit)
LoRA adapters:       ~20 MB
LoRA gradients:      ~20 MB
LoRA optimizer:      ~80 MB
Activations:         ~8000 MB (same)
-----------------------------------
TOTAL:              ~8.6 GB  (23% reduction)

PEFT + All Optimizations

Base model (8-bit):  ~125 MB
LoRA adapters:       ~20 MB
LoRA optimizer:      ~80 MB
Activations (gc):    ~2000 MB (gradient checkpointing)
-----------------------------------
TOTAL:              ~2.2 GB  (80% reduction!)

Performance Benchmarks

Configuration	Trainable Params	VRAM	Training Speed	Quality
Full Fine-Tuning	124M (100%)	11 GB	1.0× (baseline)	100%
LoRA r=4 Minimal	250K (0.2%)	9 GB	1.3×	85%
LoRA r=8 Minimal	500K (0.4%)	9.5 GB	1.25×	92%
LoRA r=16 Minimal	1M (0.8%)	10 GB	1.2×	97%
LoRA r=16 Balanced	2M (1.6%)	10.5 GB	1.15×	99%
LoRA r=32 Full	8M (6.5%)	11 GB	1.1×	99.5%

Key Findings: - LoRA r=16 with balanced modules achieves 99% quality at 1.6% parameters - Speed improvements come from fewer gradients to compute - VRAM savings enable larger batch sizes (→ better quality)

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Best Practices

1. Start with Recommended Defaults

use_peft: true
peft_method: "lora"
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules: "q_proj,v_proj"  # or "q_proj,k_proj,v_proj,o_proj"

2. Tune Rank Based on Task Complexity

• Simple tasks (sentiment, classification): r=4-8
• Medium tasks (summarization, QA): r=8-16
• Complex tasks (creative writing, reasoning): r=16-32
• Very complex (code generation, math): r=32-64

3. Adjust Alpha with Rank

• Maintain alpha = 2 × r ratio
• Increase alpha if adapters learn too slowly
• Decrease alpha if training becomes unstable

4. Use Dropout for Small Datasets

• dataset < 1K samples: dropout = 0.2-0.3
• dataset 1K-10K: dropout = 0.1
• dataset 10K-100K: dropout = 0.05
• dataset > 100K: dropout = 0.0-0.05

5. Target Modules Strategy

• Always start with: q_proj,v_proj
• If underfitting, add: k_proj,o_proj
• If still underfitting, add: gate_proj,up_proj,down_proj
• Never remove: q_proj,v_proj (most impactful)

6. Combine with Other Optimizations

PEFT works great with: - ✅ Gradient checkpointing (memory) - ✅ AMP/mixed precision (speed + memory) - ✅ 8-bit optimizers (memory) - ✅ CPU offloading (extreme memory savings) - ✅ Flash Attention (speed)

7. Monitor Training Metrics

• Trainable params should be < 5% of total
• Loss convergence should be similar to full fine-tuning
• VRAM usage should be 20-50% lower
• Training speed should be 1.1-1.5× faster

8. Save and Merge Adapters

# Save adapter only (small file ~10-30 MB)
model.save_pretrained("path/to/lora_adapter")

# Merge adapter into base model (optional)
merged_model = model.merge_and_unload()
merged_model.save_pretrained("path/to/merged_model")

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Testing & Validation

Validation Checklist

✅ Configuration Validation

• [ ] use_peft correctly enables/disables PEFT
• [ ] All three methods (lora, adalora, ia3) work
• [ ] Target modules parse correctly
• [ ] Invalid configurations raise helpful errors

✅ Training Validation

• [ ] Model trains successfully with PEFT
• [ ] Loss decreases over training
• [ ] Gradients flow only to adapter parameters
• [ ] Checkpoints save correctly

✅ Memory Validation

• [ ] VRAM usage is lower than full fine-tuning
• [ ] Larger batch sizes fit in memory
• [ ] Gradient checkpointing + PEFT works

✅ Quality Validation

• [ ] Eval metrics comparable to full fine-tuning
• [ ] Model output quality is good
• [ ] No catastrophic forgetting
• [ ] Adapters load correctly for inference

Common Issues & Solutions

Issue: "No trainable parameters"

Cause: Target modules don't match model architecture
Solution: Use q_proj,v_proj for HRM models

Issue: "PEFT library not available"

Cause: peft package not installed
Solution: pip install peft>=0.11.1

Issue: "Training loss doesn't decrease"

Cause: lora_alpha too low or rank too small
Solution: Increase lora_alpha or lora_r

Issue: "Out of memory with PEFT enabled"

Cause: Other factors (batch size, sequence length)
Solution: Reduce batch size or enable gradient checkpointing

Issue: "Training is unstable"

Cause: lora_alpha too high
Solution: Reduce lora_alpha or add more dropout

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Commands (CLI)

PowerShell examples for enabling PEFT with aios hrm-hf train-actv1:

Minimal (q,v only — best VRAM efficiency):

.venv\Scripts\python.exe -m aios.cli.aios hrm-hf train-actv1 `
    --model gpt2 `
    --dataset-file training_data/curated_datasets/test_sample.txt `
    --steps 200 `
    --batch-size 4 `
    --halt-max-steps 1 `
    --use-peft `
    --peft-method lora `
    --lora-r 16 `
    --lora-alpha 32 `
    --lora-dropout 0.05 `
    --lora-target-modules "q_proj,v_proj" `
    --log-file artifacts/brains/actv1/metrics.jsonl

Balanced (q,k,v,o):

.venv\Scripts\python.exe -m aios.cli.aios hrm-hf train-actv1 `
    --model gpt2 `
    --dataset-file training_data/curated_datasets/test_sample.txt `
    --steps 200 `
    --batch-size 4 `
    --halt-max-steps 1 `
    --use-peft `
    --peft-method lora `
    --lora-r 16 `
    --lora-alpha 32 `
    --lora-dropout 0.05 `
    --lora-target-modules "q_proj,k_proj,v_proj,o_proj" `
    --log-file artifacts/brains/actv1/metrics.jsonl

AdaLoRA variant:

.venv\Scripts\python.exe -m aios.cli.aios hrm-hf train-actv1 `
    --model gpt2 `
    --dataset-file training_data/curated_datasets/test_sample.txt `
    --steps 200 `
    --batch-size 4 `
    --halt-max-steps 1 `
    --use-peft `
    --peft-method adalora `
    --lora-r 16 `
    --lora-alpha 32 `
    --lora-dropout 0.1 `
    --lora-target-modules "q_proj,k_proj,v_proj,o_proj" `
    --log-file artifacts/brains/actv1/metrics.jsonl

Notes: - Flags are wired in src/aios/cli/hrm_hf_cli.py and applied in src/aios/cli/hrm_hf/model_precision.py. - Use --amp and --gradient-checkpointing with PEFT for best VRAM efficiency.

Inputs & Outputs

Inputs: - Base model: --model <hf-id-or-local-path> - Dataset: --dataset-file <path or hf://…> - PEFT toggles: --use-peft, --peft-method, --lora-r, --lora-alpha, --lora-dropout, --lora-target-modules

Outputs: - Brain bundle under artifacts/brains/actv1/<brain-name>/ - Metrics JSONL at artifacts/brains/actv1/metrics.jsonl - Optional PEFT adapter save/merge (see code snippet below)

Try it (PowerShell)

Quick dry-run to verify PEFT wiring:

.venv\Scripts\python.exe -m aios.cli.aios hrm-hf train-actv1 `
    --model gpt2 `
    --dataset-file training_data/curated_datasets/test_sample.txt `
    --steps 1 `
    --batch-size 2 `
    --halt-max-steps 1 `
    --use-peft `
    --peft-method lora `
    --lora-r 8 `
    --lora-alpha 16 `
    --lora-target-modules "q_proj,v_proj" `
    --log-file artifacts/brains/actv1/metrics.jsonl

Expected log lines include a {"peft": "enabled", ...} entry with trainable parameter percentages < 5%.

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Conclusion

LoRA/PEFT in AI-OS provides a powerful, efficient way to fine-tune models with: - 95-99% fewer trainable parameters - 40-60% VRAM savings - Faster training speeds - Comparable or better quality

Recommended Starting Point

use_peft: true
peft_method: "lora"
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules: "q_proj,v_proj"

Then adjust based on: - VRAM availability → increase r or target modules - Task complexity → increase r and alpha - Dataset size → adjust dropout - Quality needs → add more target modules

Further Reading

• LoRA Paper
• PEFT Documentation
• AdaLoRA Paper
• IA3 Paper

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Last Updated: October 19, 2025
Version: 1.0
AI-OS Version: Compatible with all ACTv1 models

See also: Memory Optimization • Core Training • GUI Features