Train Robotic AI Models using Qualia

Fine-tune robot foundation models on cloud GPUs — π0.5, π0, GR00T, SmolVLA, ACT, and more.

Qualia

Fine-tune Vision-Language-Action (VLA) models for robotics on cloud GPUs.

Setup

1. Sign up at app.qualiastudios.dev
2. Create an API key (Settings → API Keys)
3. Set the env var:

   export QUALIA_API_KEY="your-api-key"
   

When Someone Asks to Train a Model

They probably won't give you everything upfront. Here's what you need and how to get it:

1. Dataset — ask for their HuggingFace dataset ID (e.g. your-org/your-dataset)
2. Model type — if they don't specify, run models and help them choose:
   • Quick prototyping → suggest ACT (fast, no base model needed)
   • Production quality → suggest π0.5 or π0
   • Humanoid robots → suggest GR00T N1.5
   • Resource-conscious → suggest SmolVLA
3. Training duration — if unspecified, suggest 2–4 hours for a first run
4. Camera mapping — run dataset-keys on their dataset, then models to see required slots, and map them automatically. Confirm with the user before launching.

If the user already has a project, use it. Otherwise create one.

When Things Go Wrong

Symptom	Likely cause	Fix
Job stuck at credit_validation	Insufficient credits	Run credits, tell user to top up
Fails at dataset_preprocessing	Bad camera mapping or invalid dataset	Re-check dataset-keys output, verify mapping
Fails at instance_booting	GPU capacity issue	Try a different instance type or region
Job failed with no clear error	Check phase events	Run status <job_id> and read the event messages

Always run status <job_id> and share the full phase history with the user when debugging.

Quick Start

# See what models are available (always check — new ones are added regularly)
python3 {baseDir}/scripts/qualia.py models

# Check GPU options and pricing
python3 {baseDir}/scripts/qualia.py instances

# Check your credit balance
python3 {baseDir}/scripts/qualia.py credits

Train a Model

# 1. Discover image keys in your dataset
python3 {baseDir}/scripts/qualia.py dataset-keys your-org/your-dataset

# 2. Create a project
python3 {baseDir}/scripts/qualia.py project-create "My Robot"

# 3. Launch training
python3 {baseDir}/scripts/qualia.py finetune <project_id> <vla_type> your-org/your-dataset 4 \
  '{"cam_1": "observation.images.top"}' \
  --model <base_model_id> \
  --name "My run"

# 4. Monitor
python3 {baseDir}/scripts/qualia.py status <job_id>

Notes:

• Run models first to see which VLA types require --model and which don't
• Camera mappings map model slots (from models) to dataset image keys (from dataset-keys)
• Smart camera mapping: The API returns generic slot names (cam_1, cam_2, cam_3) but the underlying models have a specific input order. Map semantically using these known orders:
  • π0.5 / π0: cam_1 = base/overview camera, cam_2 = left wrist/arm, cam_3 = right wrist/arm
  • GR00T N1.5: cam_1 = base/overview camera, cam_2 = left wrist/arm, cam_3 = right wrist/arm
  • ACT / SmolVLA: cam_1 = primary camera, cam_2/cam_3 = secondary views
  • Fuzzy-match dataset keys to these roles: context_camera or base_0 → cam_1; left_wrist ≈ left_arm → cam_2; right_wrist ≈ right_arm → cam_3
• Omit --model for types that don't support custom models
• Estimate cost before launching: run instances to get credits/hr, multiply by hours. Tell the user the estimated cost before confirming.
• Dataset IDs on HuggingFace are case-sensitive — double-check the exact ID

Manage Jobs & Projects

python3 {baseDir}/scripts/qualia.py projects                     # List projects and jobs
python3 {baseDir}/scripts/qualia.py status <job_id>              # Job status and phase history
python3 {baseDir}/scripts/qualia.py cancel <job_id>              # Cancel a running job
python3 {baseDir}/scripts/qualia.py project-delete <project_id>  # Delete a project

Custom Hyperparameters

# Get defaults
python3 {baseDir}/scripts/qualia.py hyperparams <vla_type> [model_id]

# Validate overrides
python3 {baseDir}/scripts/qualia.py hyperparams-validate <vla_type> '{"learning_rate": 1e-4}'

# Use in training
python3 {baseDir}/scripts/qualia.py finetune ... --hyper-spec '{"learning_rate": 1e-4, "num_epochs": 50}'

Finetune Flags

Flag	Description
--model <id>	Base model ID (required for some VLA types)
--name <str>	Job display name
--instance <id>	GPU instance type
--region <name>	Cloud region
--batch-size <n>	Batch size (1–512, default 32)
--hyper-spec '<json>'	Custom hyperparameters
--rabc <model_path>	Enable RA-BC with SARM reward model (HF path)
--rabc-image-key <k>	Image key for reward annotations
--rabc-head-mode <m>	RA-BC head mode (e.g. sparse)

RA-BC (Reward-Aware Behavior Cloning)

Use a trained SARM reward model to weight training samples. Supported on smolvla, pi0, pi05.

python3 {baseDir}/scripts/qualia.py finetune \
  <project_id> pi0 your-org/your-dataset 4 \
  '{"cam_1": "observation.images.top"}' \
  --model lerobot/pi0 \
  --rabc your-org/sarm-reward-model \
  --rabc-image-key observation.images.top \
  --rabc-head-mode sparse

Job Phases

queuing → credit_validation → instance_booting → instance_activation → instance_setup → dataset_preprocessing → training_running → model_uploading → completed

Terminal: completed, failed, cancelled

Live Docs

For the latest models, endpoints, and capabilities — always check the live documentation:

• LLM context: docs.qualiastudios.dev/llms.txt
• API reference: dev-docs.qualiastudios.dev/api/reference
• SDK: docs.qualiastudios.dev/sdk/overview
• Guides: docs.qualiastudios.dev/global/guides

Links

• Platform: https://app.qualiastudios.dev
• Docs: https://docs.qualiastudios.dev
• API: https://api.qualiastudios.dev (auth via X-API-Key header)