Training

Fine-tune models with LoRA, QLoRA, SFT, DPO, or Full training — plus pure-Rust layer surgery

Training (06)

The Training module provides two modes: Fine-Tune for GPU-accelerated model training, and Layer Surgery for pure-Rust tensor operations that don’t require Python or a GPU.

Fine-tuning requires Python 3.10+ and a one-time environment setup. Layer surgery works immediately with no dependencies.

Fine-Tune Mode

Training Methods

Method	Description	VRAM Needed
LoRA	Low-Rank Adaptation — efficient adapter training	6–12 GB
QLoRA	Quantized LoRA — 4-bit base model with LoRA adapters	4–8 GB
SFT	Supervised Fine-Tuning — standard training on instruction datasets	8–24 GB
DPO	Direct Preference Optimization — learn from chosen/rejected pairs	8–24 GB
Full	Full parameter update — maximum quality, highest VRAM	16–48 GB

VRAM Presets

LOW VRAM

~4 GB — QLoRA, rank 8, 256 seq length

BALANCED

~6 GB — QLoRA, rank 16, 512 seq length

QUALITY

~12 GB — LoRA, rank 32, 1024 seq length

MAX QUALITY

~24 GB — LoRA, rank 64, 2048 seq length

Hyperparameters

Full control over all training parameters:

Parameter	Description
Learning Rate	Step size for gradient descent
Epochs	Number of full passes through the dataset
Batch Size	Samples per training step
Gradient Accumulation	Effective batch size multiplier
Max Sequence Length	Maximum token length per sample
Warmup Steps	Linear warmup steps at start
Weight Decay	L2 regularization strength
Save Steps	Checkpoint save interval
LoRA Rank	Rank of low-rank matrices (8–64)
LoRA Alpha	Scaling factor for LoRA
LoRA Dropout	Dropout probability on adapters
Quantization Bits	4 or 8 (for QLoRA)
DPO Beta	KL penalty coefficient (for DPO)

Capability-Targeted Layer Selection

Instead of fine-tuning all layers, target specific model capabilities:

Capability	Affected Layers	What It Trains
Tool Calling	Upper-mid	API/function calling ability
Reasoning / CoT	Mid-upper	Chain-of-thought reasoning
Code Generation	Upper-mid	Code writing and understanding
Mathematics	Mid	Mathematical reasoning
Multilingual	Early-mid	Multi-language support
Instruction Following	Mid	Adherence to instructions
Safety & Alignment	Final	Safety behavior adjustment

Target Module Detection

ForgeAI auto-detects available LoRA target modules from the model architecture:

Module	Component
q_proj	Query projection (Attention)
k_proj	Key projection (Attention)
v_proj	Value projection (Attention)
o_proj	Output projection (Attention)
gate_proj	Gate projection (MLP)
up_proj	Up projection (MLP)
down_proj	Down projection (MLP)

Dataset Support

• Auto-detection of templates: Alpaca, ShareGPT, ChatML, DPO pairs, Text, Prompt/Completion
• Formats: JSON, JSONL, CSV, Parquet
• Preview: View dataset rows and column structure before training

Live Training Dashboard

During training, a real-time dashboard shows:

• Epoch and step progress
• Loss value with step-by-step loss history
• Learning rate (with warmup visualization)
• GPU VRAM usage
• ETA / time remaining
• Option to merge adapter back into base model after completion

Workflow

1. Setup environment

   First time only: ForgeAI checks for Python, creates a venv, installs PyTorch + PEFT + TRL.

2. Select model

   Browse for a GGUF or SafeTensors model (or folder).

3. Select dataset

   Browse for a training dataset. ForgeAI auto-detects format and template.

4. Choose method and preset

   Pick a training method and VRAM preset. Optionally target specific capabilities.

5. Train

   Click START TRAINING and monitor real-time progress.

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Layer Surgery Mode

Pure Rust tensor operations — no Python or GPU required.

Operations

Operation	Description
Remove Layers	Select and strip layers to reduce model size
Duplicate Layers	Clone layers at specific positions to increase depth

Features

• Rich Layer Table — memory breakdown per layer with component bars (attention/MLP/norm %)
• Tensor-Level Inspection — expand any layer to see every tensor’s dtype, shape, and memory
• Surgery Preview — shows final layer count before execution
• Format Support — works with both SafeTensors directories and GGUF files
• Auto-Update — automatically updates config.json / GGUF metadata with new layer counts

Workflow

1. Select model

   Browse for a GGUF file or SafeTensors folder

2. Load layer details

   Click LOAD LAYER DETAILS to see all layers with tensor-level breakdown

3. Select operations

   Check layers to remove, or add layers to duplicate at specific positions

4. Execute

   Click RUN SURGERY. The output is a new model file — the original is never modified.