LLM Training Dashboard - (AI-Augmented Software Engineering)

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📊 LLM Training Pipeline Presentation

Download the complete PowerPoint presentation covering the LLM creation and Ollama deployment pipeline

🚀 Train Your Model

📂 Upload Training Data

Upload a .txt, .json, or .jsonl training file. JSON datasets can use records like {"instruction": "...", "output": "..."}.

📊 Training Data Analysis

📄 File Statistics

Size:

Lines:

Words:

🔤 Tokenization

Total tokens:

Unique tokens:

Vocab coverage:

🧠 Model Utilization

Model params:

Embedding use:

Training seqs:

⏱️ Estimated Training Time

1 epoch

3 epochs

5 epochs

10 epochs

💡 Recommendations

⚙️ Training Configuration

Epochs

Batch Size

Learning Rate e.g. 0.00001 for 1e-5

LoRA Configuration

Training Mode QLoRA uses bitsandbytes on NVIDIA CUDA and switches to an Apple-native MLX-LM path on Apple Silicon for real quantized adapter training. Recommended defaults: 5 epochs, batch size 4, QLoRA, native profile.

Training Profile Native keeps each backend's preferred defaults. Comparable aligns sequence length, dataset packing, and adapter scope more closely so Apple and CUDA runs are easier to compare.

LoRA Rank

LoRA Alpha

LoRA Dropout

Save merged full model output Keep this enabled for easiest inference and export. Disable it to save only the LoRA adapter and reduce disk usage.

🔄 Resume from Checkpoint (Optional)

Continue training from a saved checkpoint instead of starting from scratch.

Recommended resets the form to the dashboard baseline. Export saves the current dashboard settings as a versioned JSON config. Load applies a previously saved config back into the form and restores the referenced training file when available.

⚠️ Please upload a training data file first

💻 System Status

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CPU

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Memory (RAM)

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0 GB / 0 GB available

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GPU

Checking...

No GPU detected or PyTorch not installed

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Device Information

Platform: -

Architecture: -

Processor: -

Python: -

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Utilization Details

CPU Cores

Disk I/O

📖 Read

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✏️ Write

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Network I/O

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📥 Received

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Last updated: Never • Updates every 2 seconds

🤖 Chat with Your Model

💬 Generate Text

Chat Source Use a model loaded from this workspace or switch to the remote Ollama server.

Chat Model

Select the exact local model directory or remote Ollama model tag the chat tab should use.

Enter your prompt:

Dataset Support

Return exact training answer on exact prompt match Model only Use matching dataset entry as support

Exact mode returns the saved training answer verbatim for exact prompt matches. Context mode still queries the model, but includes the matched dataset entry as supporting context.

Max Length (tokens) Higher values = longer responses (50-2000 tokens)

Fact vs Fiction

Fact 0.20 • mostly factual Fiction

Lower values reduce creative drift. Set this near 0 for deterministic factual answers, especially on NVIDIA.

🦙 Import Model to Ollama

📦 Import GGUF Model

ℹ️ About: This will import your trained model into Ollama, allowing you to run it locally using the ollama run command.

⚠️ No GGUF file? If you don't see any GGUF files below, you need to convert your trained model first.

Select GGUF Model File:

Model Name: This will be the name you use with ollama run <name>

System Prompt (optional):

Temperature: Controls randomness (0.0 = deterministic, 1.0+ = creative)

☁️ Cloud deploy: Use the second button to upload the selected GGUF and create the model on ollama.ayestaran.dev. This requires SSH access from the dashboard host to the server.

📝 Import Output

☁️ Ollama Cloud Server Admin

ℹ️ About: Manage models on ollama.ayestaran.dev. Listing uses the remote tags endpoint, and deletion uses SSH access from the dashboard host.

📝 Admin Output

📊 Training Artifacts

Loading artifacts...

📊 3D Model Visualizations

Interactive 3D visualizations of your model's training process, checkpoints, embeddings, and layer structures.

Checking for visualizations...

🛠️ Technical Stack

Comprehensive overview of the technologies, frameworks, and tools powering this LLM training dashboard.

🧠 Core Deep Learning Framework

PyTorch 2.0+

Primary deep learning framework powering the transformer architecture, training loops, and model optimization.

Transformers 4.30+

Hugging Face Transformers library for model architecture, tokenization, and pre-trained model integration.

CUDA Toolkit

GPU acceleration support for high-performance model training with NVIDIA graphics cards.

Apple Silicon (MPS): Native GPU acceleration for the custom M-series ARM-based processors, via Metal Performance Shaders. PyTorch uses MPS for GPU support on Apple Silicon.

🏗️ Custom Transformer Implementation

Core Components (transformer.py)

Multi-Head Attention - Scaled dot-product attention with multiple heads for parallel processing
Positional Encoding - Sinusoidal position embeddings for sequence ordering
Feed-Forward Networks - Position-wise fully connected layers with GELU activation
Transformer Layers - Encoder and decoder layers with residual connections and layer normalization
Model Variants - Support for both encoder-decoder and decoder-only architectures

Training Utilities (train_utils.py)

Gradient Accumulation - Memory-efficient training with large batch sizes
Mixed Precision Training - FP16 automatic mixed precision for faster training
Learning Rate Scheduling - Cosine annealing and warmup strategies
Checkpoint Management - Save/load model states with SafeTensors format
Text Generation - Temperature, top-k, and top-p sampling strategies

🌐 Web Dashboard & Visualization

Flask 3.0+

Lightweight web framework serving the training dashboard API and real-time status updates.

Plotly 5.20+

Interactive 3D visualizations for model checkpoints, embeddings, and layer structures.

Marked.js

Markdown parsing for rendering documentation and workflow guides directly in the dashboard.

📊 Data Processing & System Monitoring

NumPy 1.24+

Numerical computing library for efficient array operations and data manipulation.

PSUtil 5.9+

Real-time system and process monitoring for CPU, memory, GPU, and disk usage tracking.

TQDM 4.65+

Progress bars for training loops and batch processing with ETA estimates.

🦙 Model Export & Deployment

SafeTensors 0.4+

Fast and secure model serialization format for checkpoint saving and model conversion.

Ollama Integration

Export trained models to GGUF format and import directly into Ollama for local inference.

llama.cpp

Convert PyTorch models to GGUF format for efficient CPU/GPU inference with quantization.

🔧 Optional Tools & Extensions

TensorBoard 2.12+

Training visualization and metrics tracking for loss curves, learning rates, and gradients.

Weights & Biases

Experiment tracking and collaboration platform for machine learning projects.

Scikit-learn 1.2+

Data preprocessing utilities and evaluation metrics for model performance analysis.

🎯 System Architecture

Data Pipeline: Text/JSONL Input → Tokenization → Dataset Creation → DataLoader

Training Loop: Forward Pass → Loss Calculation → Backpropagation → Optimizer Step → Checkpoint Save

Model Export: PyTorch Model → SafeTensors → GGUF Conversion → Ollama Import

Dashboard: Flask API ↔ Real-time Updates ↔ 3D Visualizations ↔ System Monitoring

🚀 Built for Production-Ready LLM Training

Architecture, Design and Development by Franz Ayestaran / Enhanced Pair Programming with Claude Code

🔒 Enter Password to Continue

🤖 LLM Training Dashboard - (AI-Augmented Software Engineering)

📊 LLM Training Pipeline Presentation

🚀 Train Your Model

📂 Upload Training Data

📝 Edit Training Data

📊 Training Data Analysis

📄 File Statistics

🔤 Tokenization

🧠 Model Utilization

⏱️ Estimated Training Time

💡 Recommendations

✓ Ready to Train

⚙️ Training Configuration

LoRA Configuration

🔄 Resume from Checkpoint (Optional)

📊 Training Progress

🧪 Epoch Quality Check

💻 System Status

CPU

Memory (RAM)

GPU

Device Information

Utilization Details

CPU Cores

Disk I/O

Network I/O

🤖 Chat with Your Model

💬 Generate Text

📝 Generated Text

🦙 Import Model to Ollama

📦 Import GGUF Model

📝 Import Output

☁️ Ollama Cloud Server Admin

No Cloud Models Found

📝 Admin Output

📊 Training Artifacts

Total Items

Total Size

Categories

⚠️ Warning

No Training Artifacts Found

📊 3D Model Visualizations

No Visualizations Available

🛠️ Technical Stack

🧠 Core Deep Learning Framework

PyTorch 2.0+

Transformers 4.30+

CUDA Toolkit

🏗️ Custom Transformer Implementation

Core Components (transformer.py)

Training Utilities (train_utils.py)

🌐 Web Dashboard & Visualization

Flask 3.0+

Plotly 5.20+

Marked.js

📊 Data Processing & System Monitoring

NumPy 1.24+

PSUtil 5.9+

TQDM 4.65+

🦙 Model Export & Deployment

SafeTensors 0.4+

Ollama Integration

llama.cpp

🔧 Optional Tools & Extensions

TensorBoard 2.12+

Weights & Biases

Scikit-learn 1.2+

🎯 System Architecture