Documentation

1. Create a dataset

Create a CSV file with two columns: text (your input text) and label (the category). Example:

text,label
"Das ist ein positiver Review",positive
"Mir hat das nicht gefallen",negative
"Sehr zufrieden mit dem Produkt!",positive

2. Choose a model

For text classification we recommend:

• •bert-base-uncased – good for English, fast
• •deepset/gbert-base – good for German
• •distilbert-base-uncased – smaller & faster than BERT

3. Set parameters

Recommended starting parameters for this example:

4. Observe training

During training you'll see:

• 📈 Training Loss - should decrease steadily
• 📊 Validation Accuracy - should increase toward 90%+
• ⚡ GPU Utilization - should be around 80-100%
• 💾 Time Remaining - estimated training time

Features

Importing a model

How to import a model from HuggingFace:

1. 1.Open the Model Manager tab
2. 2.Click "New Model" or "HuggingFace Import"
3. 3.Enter the model ID (e.g. meta-llama/Llama-3-8B)
4. 4.Optional: provide a HuggingFace token for private/gated models
5. 5.Click Download – FrameTrain automatically downloads all necessary files

Recommended starter models

Training Workflow

Training Controls

• Start training: Starts training with the current settings. All parameters are validated before starting.
• Pause / stop training: Training can be stopped at any time. The last checkpoint is preserved.
• Resume training: After a stop, training can be resumed from the last checkpoint.

Quick Presets

FrameTrain offers preconfigured training profiles for common use cases:

Supported Formats

How to upload your dataset

1. 1.Click "Dataset" in the left panel
2. 2.Choose "Upload File" or "HuggingFace Dataset"
3. 3.Select your CSV/JSONL file
4. 4.FrameTrain validates the file and shows a preview
5. 5.Configure the text column and label column

Dataset preparation

Important notes for preparing your data:

• Minimum size: At least 100 examples per class for meaningful results.
• Class balance: Try to have roughly equal numbers of examples per class.
• Text length: Texts can vary, but very short texts (<5 words) can hurt quality.
• Train/val split: FrameTrain splits the dataset automatically (80/20). You can adjust the split manually.

Key Metrics

Common Problems

Features

• Direct input: Enter any text and see the model's prediction immediately.
• Confidence scores: Shows the probability for each class.
• Batch testing: Test multiple texts at once from a file.
• Version comparison: Compare predictions across different model versions.

Example

What gets versioned?

After every training run, FrameTrain automatically saves: model weights, training parameters, metrics, dataset reference, and a timestamp.

Versioning Workflow

Training History

In the history panel you can see all past training runs with:

• •Date & time
• •Model & dataset used
• •Final loss and accuracy
• •Hyperparameters used
• •Training duration

Fine-Tuning vs. Training from Scratch

Epochs & Batches

Epoch: One complete pass through the entire dataset. Batch: A subset of the dataset processed simultaneously. Steps: Number of parameter updates = dataset size / batch size.

Loss & Accuracy

Key Hyperparameters

What is LoRA?

LoRA (Low-Rank Adaptation) is a method for adapting language models with a fraction of the VRAM of full fine-tuning. Instead of updating all model parameters, LoRA inserts small "adapter" matrices.

These adapters are trained while the original base model stays frozen. Result: 70-90% less VRAM with comparable quality.

LoRA vs. Full Fine-Tuning

LoRA Configuration

CSV Format

Simplest format. First row = header. Required fields: text, label.

text,label
"Dies ist positiv",positive
"Das gefällt mir nicht",negative
"Sehr gut!",positive
"Schrecklich",negative

JSONL Format

One JSON object per line. More flexible than CSV for complex structures.

{
  "text": "Dies ist positiv",
  "label": "positive",
  "source": "review"
}
{
  "text": "Das gefällt mir nicht",
  "label": "negative",
  "source": "comment"
}

Best Practices

• Data volume: At least 100, ideally 500+ examples per class.
• Class balance: Similar number of examples per class for balanced training.
• Splitting datasets: 80% training, 20% validation. FrameTrain does this automatically.
• Cleaning text: Remove HTML tags, special characters, double spaces.
• Consistent labels: Label spelling must be consistent ("positive" != "Positive").

Live Monitoring Panel

During training, FrameTrain shows the following real-time information:

• GPU utilization: VRAM usage and GPU temperature in real time
• Loss curves: Training loss and validation loss as interactive line charts
• Accuracy chart: Accuracy trend and gradient norm
• Time estimate: Elapsed time and estimated time remaining

Automatic Checkpoints

FrameTrain automatically saves checkpoints:

• At the end of every epoch
• When validation loss improves (best model saving)
• On manual stops
• As complete model snapshots

Batch Size Optimization

Batch size is the most important factor for VRAM usage and training speed:

LoRA vs. Full Fine-Tuning

For most use cases, LoRA is the better choice:

Gradient Checkpointing

Enable gradient checkpointing in the training settings to save ~30-40% VRAM. Training becomes a bit slower, but you can train larger models.

Example: 7B model with LoRA: 18 GB VRAM → with gradient checkpointing: 12 GB VRAM

NVIDIA CUDA Setup

FrameTrain detects NVIDIA GPUs automatically. Requirements:

Apple Silicon (M1-M4)

Apple Silicon is fully supported – no additional configuration needed. FrameTrain automatically uses Metal Performance Shaders (MPS) for GPU acceleration.

Supported Hardware

Export Formats

Deployment Options

• Local: Use the model directly in FrameTrain or a local application.
• llama.cpp: GGUF export for efficient inference on CPU or GPU with llama.cpp.
• HuggingFace: SafeTensors export for direct loading with HuggingFace Transformers.
• Ollama: GGUF export for easy deployment with Ollama as a local LLM server.

Common Errors

All Settings

Dark Themes

Default for evening and nighttime work:

Light Themes

For working in bright daylight:

Available Presets

Automatic Updates

FrameTrain automatically checks for new versions at startup.

• New releases are detected automatically
• You're informed about changes
• One-click installation
• All settings are preserved

Checking your version

You can find your current version under: