Training Configuration
Full trains all parameters, LoRA/DoRA use parameter-efficient methods
Use -1 for all layers or specify number (for LoRA/DoRA)
Percentage of validation set used for quick checks
Fraction of data used for validation
When enabled, training stops if validation loss stops improving (disabled by default)
Training Estimates: Configure parameters to see estimates
Learning Rate Schedule Preview
Training Status

No training in progress

Training Sessions

Loading checkpoints...

Training Session
-
Iteration
-
Train Loss
-
Val Loss
-
Perplexity
-
Tokens/sec
-
Trained Tokens
-
Epoch
-
Memory (GB)
-
Learning Rate
-
Warmup Steps
-
LR Decay
-
Weight Decay
Training Progress
E.Time: -- R.Time: -- 0%
Top 3 Best Checkpoints

No training data available

All Available Checkpoints
This list shows all available checkpoints for the selected training session.
Model Quantization
Choose from local models or HuggingFace cache
Lower bits = smaller model, slightly lower quality
Smaller groups = better quality, larger file
Quantized Models

No quantized models yet

Quantization Progress
Ready for Quantization

Select a model and click "Start Quantization" to begin

Quantization Tips
4-bit vs 8-bit: 4-bit provides better compression (~75% size reduction) but slightly lower quality. 8-bit offers better quality with ~50% compression.
Group Size: Smaller groups (32) provide better quality but larger files. 64 is the recommended balance.
Performance: Quantized models are faster to load and use less memory, making them ideal for deployment.
Compatibility: Quantized models work with the same inference code as the original models.
Model Configuration
Temperature
Top P
Rep. Penalty
Shows text as it's generated
Generation Output

Model ready! Type a message below to start the conversation.

Loading...