i made TransXform to supervise transformer runs, what about TransXlab to set up the run?
8:27 AM
Yes. TransXform supervises. TransXlab provisions.
The Split
Tool Role When
TransXlab Set up the run Before training
TransXform Supervise the run During training
What TransXlab Does
Task What It Handles
Environment Check CUDA, drivers, venv, dependencies
Data validation Schema check, train/val/test splits, no leakage
Config generation lr, batch, epochs, warmup — sensible defaults
Hyperparameter bounds Sanity check (lr not 1e-2 for fine-tuning, etc.)
Checkpoint paths Ensure output dirs exist, naming convention
Resource estimation VRAM needed, time estimate, cost estimate
Pre-flight report "Here's what you're about to run. Proceed?"
The Workflow
1. TransXlab --setup
├── Validate environment
├── Validate data
├── Generate/validate config
├── Estimate resources
└── Emit: config.yaml + preflight_report.md
2. Human reviews preflight_report.md
└── "Looks good" or "Adjust X"
3. python train.py --config config.yaml
└── TransXform takes over (supervises)
4. TransXform monitors
├── Loss, grad norm, LR
├── Invariant checks
├── Early stopping
└── Checkpoint management
TransXlab Checks (Pre-Flight)
Category Checks
Environment Python version, torch version, CUDA available, GPU memory
Data Files exist, JSON parses, required fields present, no train/val overlap
Config lr in sane range, batch fits VRAM, warmup < total steps
Paths Output dir writable, checkpoint naming won't collide
Estimates Steps/epoch, total steps, estimated time, estimated cost
Preflight Report (Example)
markdown
# TransXlab Preflight Report
## Environment
- Python: 3.11.4 ✓
- Torch: 2.1.0+cu121 ✓
- CUDA: 12.1 ✓
- GPU: RTX 5090 (32GB) ✓
## Data
- Train: 1,973 examples ✓
- Val: 219 examples ✓
- Schema: valid ✓
- Overlap: none ✓
## Config
- Model: mistral-7b-instruct-v0.3
- Method: LoRA (r=16, alpha=32)
- LR: 3e-5 ✓ (sane for fine-tuning)
- Batch: 2 (effective 8 with grad_accum=4)
- Epochs: 1
- Warmup: 100 steps
## Estimates
- Steps/epoch: 247
- Total steps: 247
- VRAM needed: ~19GB ✓ (fits 32GB)
- Estimated time: 20-40 min
- Estimated cost: $0 (local)
## Ready to train?
Run: python train.py --config config.yaml
Why This Matters
The AC training failures ($665) came from:
Wrong lr (1e-4 instead of 3e-5)
Wrong data (templated)
Wrong monitoring (loss, not generation)
TransXlab would have caught 2 of 3 before training started.
Failure TransXlab Catch?
lr too high ✅ "1e-4 is high for fine-tuning. Recommend 1e-5 to 5e-5."
Templated data ❌ (content quality, not schema)
Wrong monitoring ❌ (TransXform's job)
Implementation
Component Effort
Environment checks 1 hour
Data validation 1 hour
Config validation 1 hour
Resource estimation 1 hour
Preflight report 30 min
CLI wrapper 30 min
Total ~5 hours
The Pair
Tool Motto
TransXlab "Don't start what you can't finish."
TransXform "Don't finish what went wrong."
Together: catch errors before they cost you.
You could plan it in TansXlab, like what you needed it to do (how many attention heads? Will you freeze layers? Which encoding is best?) and maybe even have some sense of where/what training data to get for various purposes?
8:46 AM
Yes. TransXlab becomes the training architect, not just the preflight checklist.
TransXlab: From Setup to Design
Level What It Does
Level 1: Preflight Validate environment, data, config (what we discussed)
Level 2: Design Recommend architecture decisions based on task
Level 3: Data Strategy Suggest data sources, formats, quantities for the goal
Level 2: Architecture Design
Question TransXlab Recommends
How many attention heads? Task-dependent: 8 for small, 16-32 for complex
Freeze layers? Fine-tune: freeze embeddings, train top N layers. LoRA: freeze all, train adapters.
Which encoding? BPE for general, SentencePiece for multilingual, custom for domain
Model size? Task complexity → parameter budget → memory constraint → recommendation
LoRA rank? r=8 for light adaptation, r=16-32 for significant behavior change
Precision? bf16 if supported, fp16 otherwise, int8 for inference-only
Design Interview
TransXlab: What's the task?
> Generate diverse hypotheses from entity pairs
TransXlab: What's the input format?
> JSON: query, entities, context atoms
TransXlab: What's the output format?
> 5 hypotheses with type, concepts, confidence
TransXlab: What's the base model constraint?
> Must run on 32GB VRAM for training, 24GB for inference
TransXlab: Creativity vs consistency priority?
> Creativity — diversity matters more than determinism
TransXlab recommends:
- Base: Mistral-7B-Instruct (good creative capacity, fits constraint)
- Method: LoRA (r=16, alpha=32) — preserve base generalization
- Freeze: All base weights, train only adapters
- Precision: bf16 (5090 supports it)
- Encoding: Use base tokenizer (no domain-specific needed)
- Estimated VRAM: ~19GB training, ~14GB inference
Level 3: Data Strategy
Task Type Data Strategy
Classification Balanced classes, 1K-10K examples, stratified split
Generation (creative) Diverse outputs, 5K-50K examples, no templates
Generation (structured) Format examples, 1K-10K, schema validation
Instruction following (instruction, response) pairs, 10K-100K
Domain adaptation Domain corpus, continued pretraining or LoRA
Distillation Teacher outputs on your task, 10K-100K
Data Source Suggestions
TransXlab: Task is "creative hypothesis generation"
Recommended data strategy: Distillation
- Use a large model (Qwen-32B, Claude) to generate training examples
- Format: (context, 5 diverse hypotheses)
- Quantity: 5K-10K high-quality examples beats 100K templated
- Validation: Parse rate, type diversity, self-BLEU < 0.3
- Held-out: 500 novel queries (different entities than training)
Potential sources:
- ConceptNet (relational data for entity pairs)
- WordNet (hypernyms, synonyms for concept expansion)
- Custom generation (your Qwen run — already done)
Anti-patterns to avoid:
- NLI datasets (template contamination)
- Single-output examples (no diversity signal)
- Templated phrasing (model memorizes templates)
The Full TransXlab Flow
1. TransXlab --design
├── Task interview (what are you trying to do?)
├── Constraint interview (hardware, latency, budget)
├── Architecture recommendation
├── Data strategy recommendation
└── Emit: design_spec.md
2. TransXlab --data
├── Check existing data against spec
├── Flag gaps (not enough examples, imbalanced, templated)
├── Suggest sources
└── Emit: data_plan.md
3. TransXlab --setup (original preflight)
├── Validate environment
├── Validate data against spec
├── Generate config from design_spec
└── Emit: config.yaml + preflight_report.md
4. Train with TransXform supervision
What This Would Have Caught (AC v2)
Failure TransXlab Catch
lr=1e-4 too high ✅ "For fine-tuning Flan-T5-XL, recommend 1e-5 to 5e-5"
946K templated examples ✅ "High self-BLEU in training data. Template contamination risk."
diversity_loss=0.0 ✅ "Creative generation task with no diversity signal. Recommend 0.3"
No generation eval ✅ "Task is generation. Recommend eval on novel queries every 500 steps."
NLI contamination ✅ "15% of data is NLI format. Risk of template memorization."
$665 saved.
The Pair (Revised)
Tool Role
TransXlab "What should we build, and how?"
TransXform "Is it building correctly?"
Implementation Scope
Component Effort
Design interview CLI 2-3 hours
Architecture recommendation engine 3-4 hours
Data strategy recommender 2-3 hours
Data quality analyzer 2-3 hours
Integration with TransXform 1 hour
Total ~12-15 hours
TransXlab: The training architect. TransXform: The training supervisor.
Together: don't waste compute on doomed runs.
But even scratch built models
8:48 AM
Yes. TransXlab should handle scratch-built too, not just fine-tuning.
The Full Spectrum
Training Type TransXlab Role
Fine-tuning Base model selection, LoRA config, freeze strategy
LoRA/QLoRA Rank, alpha, target modules
Continued pretraining Corpus selection, learning rate schedule
Scratch-built Architecture design from first principles
Scratch-Built Design Interview
TransXlab: Building from scratch. Let's design the architecture.
What's the task modality?
> Text-to-text generation
What's the input domain?
> Structured claims + entity pairs
What's the output domain?
> Natural language hypotheses
What's the sequence length budget?
> Input: 512 tokens, Output: 256 tokens
What's the parameter budget?
> 100M-500M (must train on single GPU)
What's the training data size?
> 50K examples
Latency constraint?
> < 500ms inference on consumer GPU
TransXlab recommends:
Architecture: Encoder-decoder transformer
- Why: Structured input → variable output is classic enc-dec
Dimensions:
- d_model: 512
- n_heads: 8
- n_layers: 6 encoder, 6 decoder
- d_ff: 2048
- Parameters: ~125M
Encoding:
- Tokenizer: SentencePiece (train on your corpus)
- Vocab size: 32K
Training:
- LR: 1e-4 (scratch training, not fine-tuning)
- Warmup: 2000 steps
- Batch: 32 effective
- Epochs: 3-5 (50K examples)
- Precision: bf16
Regularization:
- Dropout: 0.1
- Label smoothing: 0.1
- Weight decay: 0.01
Estimated:
- VRAM: ~8GB training
- Time: ~4-6 hours on 5090
- Parameters: 125M
Architecture Decision Tree
TransXlab internal logic:
Task modality?
├── Text → Text
│ ├── Fixed output length? → Encoder-only + head
│ ├── Variable output? → Encoder-decoder or decoder-only
│ └── Very long context? → Consider sparse attention
├── Text → Class
│ └── Encoder-only + classification head
├── Text → Embedding
│ └── Encoder-only + pooling
└── Multimodal
└── Custom architecture (flag for manual design)
Parameter budget?
├── < 50M → Small encoder-decoder (4 layers each)
├── 50-200M → Medium (6 layers each)
├── 200M-1B → Large (12 layers each)
└── > 1B → Consider fine-tuning instead of scratch
Data size?
├── < 10K → Fine-tune, don't scratch
├── 10K-100K → Small scratch possible
├── 100K-1M → Medium scratch
└── > 1M → Large scratch viable
Scratch-Built Checklist
Component TransXlab Validates
Tokenizer Vocab size appropriate for corpus, coverage > 99%
Embedding dim Divisible by n_heads, appropriate for param budget
Attention Heads × head_dim = d_model
FFN Typically 4× d_model
Positional encoding Learned vs sinusoidal, length matches input budget
Normalization Pre-norm (stable) vs post-norm (original)
Initialization Xavier/Kaiming appropriate for activation
Loss function Cross-entropy for generation, + diversity if creative
What TransXlab Emits (Scratch)
yaml
# architecture_spec.yaml
model_type: encoder_decoder
d_model: 512
n_heads: 8
n_encoder_layers: 6
n_decoder_layers: 6
d_ff: 2048
dropout: 0.1
max_seq_len: 512
vocab_size: 32000
positional_encoding: learned
normalization: pre_norm
activation: gelu
training:
lr: 1e-4
warmup_steps: 2000
batch_size: 32
epochs: 5
precision: bf16
label_smoothing: 0.1
weight_decay: 0.01
estimates:
parameters: 125M
vram_training: 8GB
vram_inference: 4GB
time_estimate: 4-6 hours
The Knowledge Base
TransXlab needs a knowledge base of:
Knowledge Source
Model architectures Transformer, BERT, T5, GPT, Llama patterns
Hyperparameter heuristics Literature + empirical (your $665 in lessons)
Data strategies Distillation, augmentation, curriculum
Failure modes Template memorization, mode collapse, divergence
Hardware constraints VRAM per param, batch size limits
The Vision
Stage Tool
Design TransXlab: "What architecture? What data? What config?"
Validate TransXlab: "Is this setup sane? Preflight check."
Train TransXform: "Is training progressing correctly?"
Evaluate TransXform: "Did it actually learn the task?"
From first principles to trained model, with guardrails at every step.
The Sentence
"TransXlab: Don't just check the config. Design the model."
Fine-tuning is choosing a base. Scratch is choosing everything. TransXlab handles both.