Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/tenstorrent/tt-studio/ai-modelsgit clone --depth 1 https://github.com/tenstorrent/tt-studioWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/tenstorrent/tt-studio/ai-models)<a href="https://agentmods.dev/rules/tenstorrent/tt-studio/ai-models"><img src="https://agentmods.dev/badge/rules/tenstorrent/tt-studio/ai-models.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00868 |
| Opus 5 | $0.00000 | $0.00434 |
| Sonnet 5 | $0.00000 | $0.00174 |
| Haiku 4.5 | $0.00000 | $0.00087 |
Grade A, and why
ai-models scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Model Integration Rules
Rules for implementing AI model functionality in TT Studio.
Supported Model Types
-
Chat Models (LLMs)
- Conversational AI interfaces
- Streaming response support
- Context management and history
- Real-time inference display
-
Vision Models (YOLO)
- Object detection and recognition
- Image upload and processing
- Real-time inference results
- Bounding box visualization
-
Speech Models (Whisper)
- Speech-to-text conversion
- Audio file upload support
- Real-time audio processing
- Multiple language support
-
Image Generation (Stable Diffusion)
- Text-to-image generation
- Parameter customization (steps, guidance, etc.)
- Progress tracking for generation
- Result gallery and management
-
Document Processing & RAG
- PDF, DOCX, and HTML document processing
- Vector embeddings with ChromaDB's ONNX all-MiniLM-L6-v2
- ChromaDB for similarity search and retrieval
- LangChain for document processing pipelines
- Context-aware question answering over documents
Model Deployment Guidelines
Frontend Implementation
- Provide clear model selection interfaces
- Implement proper loading states during model deployment
- Show deployment progress and status
- Handle deployment failures gracefully
- Support model switching without page reload
Backend Implementation
- Integrate with TT Inference Server for model execution
- Handle Hugging Face model downloads (requires HF_TOKEN)
- Use LangChain for document processing and RAG pipelines
- Implement ChromaDB for vector storage and similarity search
- Process documents with pypdf, python-docx, beautifulsoup4
- Generate embeddings with ChromaDB's ONNX all-MiniLM-L6-v2
- Use Docker SDK for containerized model execution
- Implement proper error handling for model load failures
- Support containerized model execution
- Monitor model resource usage with psutil
Hardware Optimization
- Leverage Tenstorrent hardware when available
- Provide performance metrics and utilization data
- Implement hardware-specific optimizations
- Support graceful fallback to CPU execution
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 130 lines · 0 tokens per session scan A 78c32a0c26b0
ai-models is a cursor rule published in the GitHub repository tenstorrent/tt-studio (49 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 868 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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