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 skills add Seeed-Projects/Seeed-Jetson-DevelopTool --skill finetune-llm-llama-factorygit clone --depth 1 https://github.com/Seeed-Projects/Seeed-Jetson-DevelopToolWrote 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/skills/seeed-projects/seeed-jetson-developtool/finetune-llm-llama-factory)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/finetune-llm-llama-factory"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/finetune-llm-llama-factory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/finetune-llm-llama-factory"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/finetune-llm-llama-factory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Privilege Escalation · line 34 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
- medium Privilege Escalation · line 93 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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 5.1 | $0.00072 | $0.00857 |
| Opus 5 | $0.00036 | $0.00428 |
| Sonnet 5 | $0.00014 | $0.00171 |
| Haiku 4.5 | $0.00007 | $0.00086 |
Grade B, and why
finetune-llm-llama-factory scanned grade B with 1 finding 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 11d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo reboot How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fine-tune LLM with Llama-Factory on Jetson
Execution model
Run one phase at a time. After each phase:
- Relay all command output to the user.
- If output contains
[STOP]→ stop immediately, consult the failure decision tree below. - If output ends with
[OK]→ tell the user "Phase N complete" and proceed to the next phase.
Prerequisites
| Requirement | Details |
|---|---|
| Hardware | Jetson device with ≥16GB RAM (tested on Orin NX 16GB and AGX Orin 64GB) |
| Peripherals | Monitor, mouse, keyboard, network (optional but recommended) |
| JetPack | 5.x or 6.x |
| Internet | Required for initial container pull |
Phase 1 — Install jetson-examples (~2 min)
pip3 install jetson-examples
sudo reboot
[OK] after reboot completes and you can log back in.
Phase 2 — Deploy Llama-Factory (~5–15 min)
Launch Llama-Factory using the one-line deployment:
reComputer run llama-factory
This pulls the container and starts the Llama-Factory service.
Once running, open a web browser and navigate to:
http://127.0.0.1:7860
(Or replace 127.0.0.1 with the Jetson's IP for remote access.)
[OK] when the Llama-Factory WebUI loads in the browser. [STOP] if the container fails to start.
Phase 3 — Start training (~18 hours for default config)
In the WebUI:
- Set Model name to
Phi-1.5(or your chosen model) - Set Dataset to
alpaca_zh(or your chosen dataset) - Keep other training parameters as default
- Click the
Startbutton
Monitor training progress in the WebUI.
[OK] when training completes and the fine-tuned model appears in the save directory.
Phase 4 — Test the fine-tuned model (~2 min)
In the Llama-Factory WebUI:
- Navigate to the Chat tab
- Load the fine-tuned model by selecting the checkpoint path
- Enter a prompt in the Input text box (e.g. a Chinese language prompt if using alpaca_zh)
- Click Submit and check the output in the Chatbot text box
[OK] when the model responds with coherent output reflecting the fine-tuning data.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 105 lines · 72 tokens per session scan B 4555dd6bf191
finetune-llm-llama-factory is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (55 stars, last pushed yesterday), licensed MIT. It adds 72 tokens to every session and 857 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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