TransXLab validates and designs LLM fine-tuning configurations before training starts. One binary. No Python. Catches the mistakes that cost you $665 and a weekend.
TransXLab runs your config through a layered analysis in under a second. Each stage builds on the last. Nothing ships to the GPU until everything passes.
Environment checks and hardware validation before anything else runs.
Architecture analysis and hyperparameter validation against 25 rules.
Training data quality analysis to catch contamination and distributional issues.
Single 3.3 MB static binary. No Python, no pip, no conda. Copy it to your server and go.
Auto-detects model architecture, parameter count, and precision from any Hub model ID.
Generates validated configs for HF Trainer, Axolotl, and LLaMA-Factory from your spec.
Estimates cost across 7 GPU tiers and 4 cloud providers before you commit to a run.
Use --fail-on warn|fail with JSON output to gate training pipelines in CI.
Feed failed training logs in. TransXLab matches against 20 failure mode signatures to tell you what went wrong.
A full fine-tune of Llama-3-8B. Every parameter was plausible. All of them were wrong. TransXLab catches every issue in under a second.
$ transxlab validate --config ac-v2.yaml TransXLab v0.1.0 // validate & design before you train == PREFLIGHT == [PASS] CUDA 12.1 detected [PASS] GPU: NVIDIA RTX 4090 (24 GB) [PASS] Disk: 847 GB free [FAIL] VRAM insufficient Required: 93.4 GB (model=16.1 + optimizer=32.1 + gradients=16.1 + activations=29.1) Available: 31.8 GB (24 GB physical + 7.8 GB shared) Recommendation: Use LoRA (r=16) to reduce to ~18.2 GB == DESIGN == [FAIL] Learning rate 1e-4 exceeds safe range for 8B full fine-tune Max recommended: 5e-5 | Optimal: 3e-5 Rule: lr_max = 1e-4 / sqrt(params_B) for full fine-tune [WARN] Epoch count 10 likely to overfit Dataset size: 2,847 samples Recommended: 2-3 epochs with eval_steps=50, early_stopping_patience=3 [PASS] Batch size 4 with gradient_accumulation_steps=8 [PASS] Weight decay 0.01 within range [PASS] Warmup ratio 0.03 appropriate == DATA STRATEGY == [WARN] Template contamination detected Self-BLEU: 0.697 (threshold: 0.5) Top repeated 4-gram: "Below is an instruction that" (87% of samples) Recommendation: Strip template wrappers, diversify instruction phrasing [PASS] Token length distribution: mean=342, std=128 [PASS] No class imbalance detected == SUMMARY == 2 FAILURES 2 WARNINGS 7 PASSED VERDICT: DO NOT TRAIN Fix VRAM and learning rate issues before proceeding. Estimated cost if run anyway: $665 across 4x A100 for ~18 hours. Run transxlab design --model meta-llama/Llama-3-8B --method lora for a corrected config.
# Linux / macOS $ curl -fsSL https://github.com/zamfir70/transxlab/releases/latest/download/transxlab \ -o /usr/local/bin/transxlab $ chmod +x /usr/local/bin/transxlab # Verify $ transxlab --version transxlab 0.1.0 (3.3 MB, zero dependencies)
# Validate an existing config $ transxlab validate --config my-run.yaml # Design a new config from scratch $ transxlab design \ --model meta-llama/Llama-3-8B \ --method lora \ --gpu "RTX 4090" # CI gate: fail pipeline on warnings $ transxlab validate --config run.yaml \ --fail-on warn --output json
$ transxlab cost --config my-run.yaml Cost Estimates (3 epochs, 2847 samples) Provider GPU $/hr Hours Total ───────────────────────────────────────────────── Lambda A100 80GB $1.10 4.2 $4.62 RunPod A100 80GB $1.64 4.2 $6.89 AWS p4d.24xl $3.93 4.2 $16.51 GCP a2-highgpu $3.67 4.2 $15.41
$ transxlab postmortem --log training.log Postmortem Analysis [MATCH] Loss Divergence @ step 1,247 Pattern: loss > 2x moving average for 50+ steps Cause: Learning rate too high after warmup Fix: Reduce lr by 3-5x or use cosine schedule [MATCH] Gradient Norm Spike @ step 1,190 Pattern: grad_norm > 10x baseline Cause: Likely precedes loss divergence Fix: Add max_grad_norm=1.0 clipping
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TransXLab validates before training. TransXform supervises during training. Together, they cover the full fine-tuning lifecycle — from config validation to live monitoring, early stopping, and checkpoint management.
Use TransXLab to design and gate your run. Hand the validated config to TransXform to execute it with live loss monitoring, automatic early stopping, and structured experiment logging.