Hyperparameter Visualizer
Map context weights interactively to conceptualize fine-tuning parameter scales.
from unsloth import FastLanguageModel
import torch
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "llama-3-8b",
max_seq_length = 2048,
load_in_4bit = True,
)
model = FastLanguageModel.get_peft_model(
model,
r = 16,
lora_alpha = 32,
lora_dropout = 0,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
)
# Training Arguments
learning_rate = 1.00e-4
effective_batch_size = 16 (4 x 4)
epochs = 3About This Tool
The Hyperparameter Visualizer is an interactive simulation suite built for machine learning engineers, AI researchers, and prompt engineers who fine-tune or sample Large Language Models (LLMs).
Understanding how fine-tuning hyperparameters interact — such as how LoRA Rank ($r$) and Alpha ($\alpha$) dictate trainable parameter counts, how exponential Learning Rate ($\eta$) scaling influences training convergence, and how Softmax Temperature ($T$) flattens vocabulary token probability distributions — is critical to avoiding gradient explosion, underfitting, or out-of-memory (OOM) GPU crashes. This tool visualizes parameter scales, simulates loss curves, computes VRAM memory footprints, and exports Unsloth and HuggingFace TRL code configurations.
How to Use
- Select Target Foundation Model: Choose between Llama-3 8B, Qwen 2.5 14B, DeepSeek-R1 32B, or Llama-3 70B parameter presets.
- Adjust Fine-Tuning Sliders: Move the exponential Learning Rate slider, set LoRA Rank ($r$) and Alpha ($\alpha$), and configure batch size and epochs.
- Observe Real-Time Visualizations: Watch the simulated training loss curve update dynamically (indicating optimal, underfitting, or divergent behavior), inspect trainable parameter percentages, and review QLoRA vs Full Fine-Tune VRAM estimates.
- Explore Temperature Probability Shift: Drag the Inference Temperature slider to visually see how token softmax probabilities flatten or sharpen.
- Export Code: Copy ready-to-paste Python code for Unsloth, HuggingFace TRL `SFTTrainer`, or OpenAI/vLLM sampling JSON payloads.
Common Use Cases & FAQ
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