Use it to autonomously run experiments, interpret results and propose new directions, on your machine or in the cloud.
from lm_lab import TransformerLM, Trainer from lm_lab.data import WikiText103 import matplotlib.pyplot as plt
model = TransformerLM(
n_layers=12, n_heads=12, d_model=768,
max_seq_len=1024, dropout=0.1,
).cuda()
print(f"Parameters: {model.num_parameters():,}")Parameters: 124,439,808
trainer = Trainer(
model,
dataset=WikiText103(),
lr=6e-4, weight_decay=0.1,
scheduler="cosine", min_lr=6e-5,
)result = trainer.train(epochs=10)
The 124.4M-parameter transformer reaches a validation loss of 2.824 on WikiText-103. All three variants use the same validation set and training budget.
Training loss decreases over the fixed training budget.
Illustrative workflow: load the TAM dataset, coordinate eight agents, prepare data in Python notebooks, and compare a 62-second pipeline against a 192-second baseline. Dataset and benchmark values are simulated.
Unify diverse datasources across files, datasets, and the web. Qualia autonomously cleans and preprocesses them.
Run workflows on a schedule to generate live reports, refined by feedback after every run.
From linear regression to deep networks, or post-train 10B-parameter foundation models on your data.
Illustrative workflow: twelve agents receive instructions through the app’s To Agent chat rows, then their experiments appear in the task map. Content and timing are simulated; the layout follows the cloud app.
Fan out agents to test features, architectures, and training strategies in parallel. Diagnose model failures and run the follow-ups.
Illustrative workflow: synthesize experiments into a writeup with figures and limitations, inspect a linked claim, and trace it to the notebook output behind the conclusion. Data and timings are simulated.
Create publication-ready papers and writeups that clearly summarize research, then share them and collaborate in chats.
Give Qualia a research goal and let it work for hours, with progress updates in Slack to steer it.
Qualia breaks research goals into focused experiments that agents work on in parallel. Agents share findings and can investigate each other’s results, using what they learn to guide the next experiments.
Qualia remembers your experiments, findings, and assumptions in a shared knowledge graph that persists across sessions. Agents build on what worked and what failed, connecting insights across your research to propose creative new ideas.
Everything the agent does or says has provenance and is traceable to a query or line of code. You can inspect the evidence behind each finding and follow the steps that led to it.
Qualia is built on primitives that run on any platform — desktop, Linux cluster, or in the cloud. It runs as an agent that manipulates Jupyter notebooks, or as a headless terminal agent.
Qualia can posttrain large LMs, intelligently understand where they are failing, and improve. Run it on Qualia Cloud to take advantage of our pool of GPUs, or use standard libraries like Tinker or Fireworks.
Qualia can preprocess large amounts of multiomics data, build repeatable, auto-improving data pipelines, and autonomously fit interpretable models over it. Use Qualia’s writeup feature to build clear, publishable reports. Qualia is HIPAA-compliant and can be run in ZDR mode.
Qualia helps you explore alternative datasets, test trading hypotheses in parallel, and understand where your models fail. Work with your existing data and code to run backtests, investigate results, and propose follow-up experiments, with every finding traceable to the analysis behind it.
Qualia works with your existing data and models to understand where predictions fail, discover useful features, and run experiments to improve them. You can delegate data preparation, feature engineering, and model iteration to coordinated agents that record their findings in a reproducible knowledge graph.
Run experiments with cloud compute in your browser, and bring your team into the same research workspace. Share notebooks, agent conversations, and evidence. Teammates can comment on findings and ask Qualia questions about the research.
Illustrative collaboration preview: share a cloud workspace with Richard as a Commenter, then Robbie, Richard, and Patrick discuss a shared notebook and ask Qualia about the results. The multi-person chat previews UI in development; messages and research data are fictional.
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