Tiny Gemma2ForCausalLM
This is a minimal model built for unit tests in the TRL library.
trl-internal-testing
Turn prompts into useful text, code, summaries, and structured answers. Estimated setup is about 15 minutes, with no local installation. From $0.17/hour.
Validated runtime
Architecture and task checked
Immutable revision
61e957bcea
Dedicated workload
Isolated model endpoint
You control runtime
Runs until you stop it
The outcome
tiny-Gemma2ForCausalLM is a validated text generation model.
GPU Vault deploys the exact validated model revision on a dedicated GPU workload, with the context limit and tensor-parallel configuration already selected for you.
Use the browser console for quick testing or connect your application through a workload-scoped API key. GPU Vault does not persist prompts or generated output from inference requests.
Example workflows
Run internal chat and knowledge workflows on a dedicated model endpoint.
Condense reports, transcripts, and long-form content into useful briefs.
Draft, explain, refactor, and review code with model-specific instructions.
Turn unstructured text into predictable JSON-shaped application data.
Create first drafts for product, support, marketing, and operations teams.
Send repeated generation jobs from your own scripts or backend services.
Results depend on the model, prompt, sampling settings, and publisher guidance.
Recommended hardware
We run this on
1× GPU · 16 GB VRAM each
This is the smallest supported topology with enough memory for the model weights, runtime overhead, and the validated context window. GPU Vault also reserves 30 GB for the immutable model files and engine data.
The compatibility classifier uses safetensors metadata, engine overhead, context memory, and tensor-parallel divisibility. It rejects unsupported architectures and models that need more than eight GPUs.
Transparent usage
Current public starting rate
$0.17/hour
The dashboard shows your exact role-based rate and live offer before approval.
There is no fixed serving duration. Billing follows active compute time from instance creation through teardown.
Review the exact GPU, base compute rate, setup estimate, and initial wallet hold. No credits are reserved from this public page.
Choose your workflow
Open a browser playground for chat and completion requests as soon as the workload is ready.
Available after the model passes its readiness check.
Connect with the OpenAI-compatible chat and completions endpoints using a GPU Vault workload key.
Rotate or revoke the workload key at any time.
Exact profile
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ExploreFrom trl-internal-testing
This is a minimal model built for unit tests in the TRL library.
Common questions
Common uses include assistants, summarization, extraction, drafting, coding, and batch text processing. The model publisher documentation below describes its specific strengths and limitations.
No. GPU Vault uses a fixed server-owned runtime configuration and the model’s immutable revision. Select a live GPU offer, explicitly approve the launch, and wait for the readiness check.
The workload runs until you stop it. Compute billing starts when the GPU instance is created and ends after teardown, with the approved rate protected by your initial and rolling wallet holds.
Approval revalidates the exact model profile, GPU offer, rate, wallet, and operational gates. If a customer-visible model or offer changes before launch, GPU Vault requires a fresh review.
GPU Vault records only endpoint, status, latency, and byte-count metrics for inference requests. Prompt content, documents, embeddings, and generated output are not persisted by the inference proxy.
Ready when you are
Choose an exact live offer, review the rate and wallet hold, then approve the launch from your dashboard.