For h company infrastructure and model engineering teams

Holo1 inference failed: image preparation, attention backend or memory?

Compare screenshot dimensions and model startup with the first runtime exception. Keep a localization-quality problem separate from a crashed worker.

Replay one resolved incident Try 30 days free

Free 30-day Team evaluation. No card or automatic subscription. Work email verification required.

Why this workflow may fit H Company

H Company releases Holo1-7B as an action vision-language model with a localization example, visual-token controls and an attention-backend option. Its current site describes later Holo releases; this evaluation is explicitly Holo1.

Read the official H Company workflow reference Read current H Company model and product context

Evaluation scope: This concerns the published Holo1-7B example, not Holo4 or every hosted agent product. Denpex is not a browser agent and does not execute or validate actions. Use approved redacted runtime logs, without private screenshots or session credentials.

Prepared from public materials. This is a proposed evaluation, not a report of an incident at H Company or an endorsement.

Selectable demo logs are shared examples, not logs from H Company. Upload a shareable incident from your own workflow to evaluate the fit.

Live diagnosis, no signup

Replay a resolved H Company workflow incident

Upload your log file. Review the selected evidence and masking, then run diagnosis. Keep the original log for any follow-up questions.

Before uploading, review cloud data handling and local options.

training_logs.txt

No log to hand? Try one:

Upload or drop a plain-text log

Upload your log file. Large files are processed in your browser. Only selected evidence is submitted when you run diagnosis.

3 free diagnoses/day

Try Denpex for 30 days. FREE.

No credit card. No automatic subscription. See whether Denpex helps with an incident you already understand.

Start your free 30-day Team evaluation

Up to 50 diagnoses a day for 30 days. Verify your work email to activate an eligible company evaluation, then replay a resolved incident and compare the evidence and next steps with your team's confirmed cause.

What a useful evaluation should establish

Compare one incident your team has already resolved with the first failure, quoted evidence and remaining questions. Begin with this question: Did visual preprocessing produce the intended model input? Only assign a process, device or recovery state when the submitted evidence establishes it.

Did visual preprocessing produce the intended model input?

Evidence to compare: Redacted image dimensions, visual-token settings and preprocessing exception.

Image size and token preparation belong to the input stage. Identify a preparation failure before treating the incident as attention-kernel or GPU-capacity exhaustion.

Did the selected attention backend initialize?

Evidence to compare: Actual framework build, attention implementation and the first import or runtime error.

The optional backend has its own compatibility boundary. Record that evidence separately from a later allocation failure during generation.

Did inference crash or return an incorrect localization?

Evidence to compare: Process exit status, retained exception and separate task-evaluation result.

An incorrect coordinate prediction is not proof of a GPU fault. Infrastructure triage and computer-use quality require different evidence.

Use Denpex alongside your existing telemetry tools to investigate submitted incident evidence. With monitoring configured on a supported plan, detected failures can trigger alerts and an automatic diagnosis from the captured evidence.

Inside Denpex

See the evidence, not just the claim

Follow the causal chain, inspect the quoted evidence and prepare the next handoff. These actual product captures use founder-account examples and the VS Code extension, with the source identified under each image. Open a capture to inspect it at full size.