deepmd-finetune-dpa3 SKILL.md skillA
deepmd-finetune-dpa3 from jinzhezenggroup/computational-chemistry-agent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add jinzhezenggroup/computational-chemistry-agent-skills/deepmd-finetune-dpa3@v1.0Pins v1.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- jinzhezenggroup/computational-chemistry-agent-skills
- Path
machine-learning-potentials/deepmd-finetune-dpa3/SKILL.md- Versions recorded
- 3
- Latest version
- v1.0, committed Jun 3, 2026
- First version recorded
- Mar 21, 2026
- Size
- 12,537 bytes
- Repository stars
- 156
- License
- LGPL-3.0
Fine-tune a DPA3 model in DeePMD-kit using the PyTorch backend. Use when the user wants to adapt a pre-trained DPA3 model to a new downstream dataset. Supports fine-tuning from a self-trained DPA3 model (.pt checkpoint), from a multi-task pre-trained model, or from a built-in pretrained model downloaded via `dp pretrained download` (e.g., DPA-3.1-3M, DPA-3.2-5M, DPA-3.3-1M). Covers single-task and multi-task fine-tuning workflows.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# DeePMD-kit Fine-tuning: DPA3 Fine-tune a pre-trained DPA3 model on a downstream dataset. This skill covers three scenarios: 1. Fine-tuning from a self-trained single-task DPA3 model 1. Fine-tuning from a multi-task pre-trained DPA3 model 1. Fine-tuning from a built-in pretrained model (e.g., DPA-3.1-3M, DPA-3.2-5M, DPA-3.3-1M) downloaded via `dp pretrained download` ## Quick Start ```bash # Fine-tune from a self-trained model dp --pt train input.json --finetune pretrained.pt --use-pretrain-script # Fine-tune from a built-in pretrained model dp pretrained download DPA-3.2-5M dp --pt train input.json --finetune /path/to/DPA-3.2-5M.pt --use-pretrain-script --model-branch OMat24 ``` ## Agent Responsibilities 1. Determine the fine-tuning scenario: - Does the user have a self-trained `.pt` model? - Does the user want to use a built-in pretrained model (DPA-3.1-3M, DPA-3.2-5M, DPA-3.3-1M, etc.)? - Is the pre-trained model single-task or multi-task? 1. If using a built-in pretrained model, download it first with `dp pretrained download`. 1. Collect the downstream training data paths and element types. 1. Generate the fine-tuning `input.json`. …
Read the whole file at its exact version.
How to install
mdr add jinzhezenggroup/computational-chemistry-agent-skills/deepmd-finetune-dpa3@sha256:10dcff16bf4f3f57The line at the top of this page pins the label v1.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_nkxekauh424gmaei)
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Versions
The registry has recorded 3 versions of this file, between Mar 21, 2026 and Jun 3, 2026, about one every 37 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0 latest | 2026-06-03 | 0392dc3 | 12,537 B | A | view · diff |
| v1.0 | 2026-03-23 | 622da72 | 12,447 B | A | view · diff |
| v1.0 | 2026-03-21 | 0a9651d | 12,438 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (12537 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
jinzhezenggroup/computational-chemistry-agent-skills · 156 stars · license LGPL-3.0 · pushed 2026-11-02 · branch master
API
GET https://markdownregistry.com/api/v1/artifacts/art_nkxekauh424gmaei GET https://markdownregistry.com/api/v1/resolve?ref=jinzhezenggroup/computational-chemistry-agent-skills/deepmd-finetune-dpa3 GET https://markdownregistry.com/api/v1/blob/10dcff16bf4f3f5791578c44e0b110c5f456c92a6d79a11c3bec0aee20b185a6
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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