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Agent skill

Deepmd Finetune Dpa3

by jinzhezenggroup in jinzhezenggroup/computational-chemistry-agent-skills

Fine-tune a DPA3 model in DeePMD-kit using the PyTorch backend.

LGPL-3.0-or-laterAuto-check passedAI & LLM Engineering

Install Deepmd Finetune Dpa3

skills CLI
$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-finetune-dpa3 -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills deepmd-finetune-dpa3 --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/machine-learning-potentials/deepmd-finetune-dpa3 .claude/skills/deepmd-finetune-dpa3 && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deepmd-finetune-dpa3
GitHub stars
148
Token cost
~3.1k tokens
SKILL.md length
702 words
Files
1
Skills in repo
62
Repo updated
First seen
Licence
LGPL-3.0-or-later

At a glance

Fine-tune a DPA3 model in DeePMD-kit using the PyTorch backend.

  • Works in 7 steps: Prepare input.json → Run Fine-tuning → Check Available Models → …
  • The user wants to adapt a pre-trained DPA3 model to a new downstream dataset
  • SKILL.md covers Quick Start, Agent Responsibilities, Scenario 1: Fine-tune from a… and Scenario 2: Fine-tune from a…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Deepmd Finetune Dpa3 is an agent skill from jinzhezenggroup/computational-chemistry-agent-skills. 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.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires deepmd-kit with PyTorch backend installed. GPU strongly recommended.

It sits in AI & LLM Engineering, covering Fine-tuning. It works with PyTorch. The repository describes itself as: Agent skills to run computational-chemistry tasks, used in OpenClaw. The licence is LGPL-3.0-or-later.

When your agent uses it

  • The user wants to adapt a pre-trained DPA3 model to a new downstream dataset
  • Tasks that involve Fine-tuning

Example prompts

  • “/deepmd-finetune-dpa3”

Requirements

  • Compatibility (from SKILL.md): Requires deepmd-kit with PyTorch backend installed. GPU strongly recommended.

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Prepare input.json
  2. Run Fine-tuning
  3. Check Available Models
  4. Download the Model
  5. Check Model Branches (if multi-task)
  6. Prepare input.json and Run Fine-tuning
  7. Freeze and Test

What it can do on your machine

Read from SKILL.md and the folder at commit 5c19e75. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash and json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.deepmodeling.com
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires deepmd-kit with PyTorch backend installed. GPU strongly recommended.

    From compatibility in the SKILL.md frontmatter.

Context cost

Deepmd Finetune Dpa3 loads about 3.1k tokens when it runs. Until then it costs about 114 tokens; SKILL.md has 702 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~114
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jinzhezenggroup/computational-chemistry-agent-skills at commit 5c19e75, republished under its LGPL-3.0-or-later licence (© jinzhezenggroup). 702 words, ~3,133 tokens.

Download SKILL.mdSave it as .claude/skills/deepmd-finetune-dpa3/SKILL.md (or your agent's skills folder).
name
deepmd-finetune-dpa3
description
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.
compatibility
Requires deepmd-kit with PyTorch backend installed. GPU strongly recommended.
license
LGPL-3.0-or-later
metadata.author
iProzd
metadata.version
1.0
metadata.repository
https://github.com/deepmodeling/deepmd-kit

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
  2. Fine-tuning from a multi-task pre-trained DPA3 model
  3. 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?
  2. If using a built-in pretrained model, download it first with dp pretrained download.
  3. Collect the downstream training data paths and element types.
  4. Generate the fine-tuning input.json.
  5. Run fine-tuning and monitor the learning curve.
  6. Freeze and test the fine-tuned model.

Scenario 1: Fine-tune from a Self-trained Single-task Model

When you have trained a DPA3 model yourself and want to adapt it to new data.

Step 1: Prepare input.json

When using --use-pretrain-script, the model architecture is inherited from the pre-trained model. You only need to specify type_map, data paths, and training parameters:

json
{
  "model": {
    "type_map": [
      "O",
      "H"
    ],
    "descriptor": {},
    "fitting_net": {}
  },
  "learning_rate": {
    "type": "exp",
    "decay_steps": 5000,
    "start_lr": 0.0001,
    "stop_lr": 3e-06
  },
  "loss": {
    "type": "ener",
    "start_pref_e": 0.2,
    "limit_pref_e": 20,
    "start_pref_f": 100,
    "limit_pref_f": 60,
    "start_pref_v": 0.02,
    "limit_pref_v": 1
  },
  "optimizer": {
    "type": "AdamW",
    "weight_decay": 0.001
  },
  "training": {
    "training_data": {
      "systems": [
        "./downstream_data/train_0",
        "./downstream_data/train_1"
      ],
      "batch_size": 1
    },
    "validation_data": {
      "systems": [
        "./downstream_data/valid_0"
      ],
      "batch_size": 1
    },
    "numb_steps": 200000,
    "gradient_max_norm": 5.0,
    "seed": 10,
    "disp_file": "lcurve.out",
    "disp_freq": 100,
    "save_freq": 2000
  }
}

Fine-tuning tips:

  • Use a smaller start_lr (e.g., 1e-4) than training from scratch (1e-3).
  • Use fewer numb_steps since the model is already pre-trained.
  • The elements in the downstream data must be a subset of the pre-trained model's type_map.
Step 2: Run Fine-tuning
bash
dp --pt train input.json --finetune pretrained.pt --use-pretrain-script

The --use-pretrain-script flag tells DeePMD-kit to inherit the model architecture from the pre-trained model, so the descriptor and fitting_net sections in input.json can be empty.

Without --use-pretrain-script, the model section in input.json must exactly match the pre-trained model's architecture.

Scenario 2: Fine-tune from a Multi-task Pre-trained Model

When the pre-trained model was trained with multiple datasets (multi-task training), you can select a specific branch to fine-tune from.

Check Available Branches
bash
dp --pt show multitask_pretrained.pt model-branch
Run Fine-tuning from a Specific Branch
bash
dp --pt train input.json --finetune multitask_pretrained.pt --model-branch CHOSEN_BRANCH --use-pretrain-script

If --model-branch is not set or set to RANDOM, a randomly initialized fitting net will be used.

Multi-task Fine-tuning (Prevent Forgetting)

To retain knowledge from the pre-trained datasets during fine-tuning, use multi-task fine-tuning. Prepare a multi-task input script:

json
{
  "model": {
    "shared_dict": {
      "type_map_all": [
        "O",
        "H",
        "C",
        "N"
      ],
      "dpa3_desc": {
        "type": "dpa3",
        "repflow": {}
      }
    },
    "model_dict": {
      "pre_data_1": {
        "type_map": "type_map_all",
        "descriptor": "dpa3_desc",
        "fitting_net": {}
      },
      "pre_data_2": {
        "type_map": "type_map_all",
        "descriptor": "dpa3_desc",
        "fitting_net": {}
      },
      "downstream": {
        "finetune_head": "pre_data_1",
        "type_map": "type_map_all",
        "descriptor": "dpa3_desc",
        "fitting_net": {}
      }
    }
  },
  "learning_rate": {
    "type": "exp",
    "decay_steps": 5000,
    "start_lr": 0.0001,
    "stop_lr": 3e-06
  },
  "loss_dict": {
    "pre_data_1": {
      "type": "ener",
      "start_pref_e": 0.2,
      "limit_pref_e": 20,
      "start_pref_f": 100,
      "limit_pref_f": 60
    },
    "pre_data_2": {
      "type": "ener",
      "start_pref_e": 0.2,
      "limit_pref_e": 20,
      "start_pref_f": 100,
      "limit_pref_f": 60
    },
    "downstream": {
      "type": "ener",
      "start_pref_e": 0.2,
      "limit_pref_e": 20,
      "start_pref_f": 100,
      "limit_pref_f": 60
    }
  },
  "training": {
    "model_prob": {
      "pre_data_1": 0.3,
      "pre_data_2": 0.3,
      "downstream": 1.0
    },
    "data_dict": {
      "pre_data_1": {
        "training_data": {
          "systems": [
            "./pre_data_1/train"
          ],
          "batch_size": 1
        }
      },
      "pre_data_2": {
        "training_data": {
          "systems": [
            "./pre_data_2/train"
          ],
          "batch_size": 1
        }
      },
      "downstream": {
        "training_data": {
          "systems": [
            "./downstream/train"
          ],
          "batch_size": 1
        },
        "validation_data": {
          "systems": [
            "./downstream/valid"
          ],
          "batch_size": 1
        }
      }
    },
    "numb_steps": 200000,
    "gradient_max_norm": 5.0,
    "disp_file": "lcurve.out",
    "disp_freq": 100,
    "save_freq": 2000
  }
}

Key points:

  • "finetune_head": "pre_data_1" specifies which branch the downstream task fine-tunes from.
  • model_prob controls the sampling probability for each dataset.
  • Pre-trained branches continue training in init-model mode; the downstream branch fine-tunes from the selected head.

Run:

bash
dp --pt train multi_input.json --finetune multitask_pretrained.pt

Freeze a specific branch:

bash
dp --pt freeze -o model_downstream.pth --head downstream

Scenario 3: Fine-tune from Built-in Pretrained Models

DeePMD-kit provides built-in pretrained models that can be downloaded directly.

Step 1: Check Available Models
bash
dp pretrained download -h

Currently available models include:

  • DPA-3.3-1M — 1M parameter DPA3 pretrained model
  • DPA-3.2-5M — latest large-scale pretrained model
  • DPA-3.1-3M — 3M parameter DPA3 pretrained model
  • DPA3-Omol-Large — large organic molecule model
Show full SKILL.md (277 more words)Show less
Step 2: Download the Model
bash
# Download to default cache directory
dp pretrained download DPA-3.1-3M

# Download to a custom directory
dp pretrained download DPA-3.1-3M --cache-dir ./models

The command prints the local path of the downloaded model file on success.

Step 3: Check Model Branches (if multi-task)
bash
dp --pt show /path/to/DPA-3.1-3M.pt model-branch
Step 4: Prepare input.json and Run Fine-tuning

The input.json is the same as Scenario 1. Use --use-pretrain-script to inherit the model architecture:

json
{
  "model": {
    "type_map": [
      "O",
      "H"
    ],
    "descriptor": {},
    "fitting_net": {}
  },
  "learning_rate": {
    "type": "exp",
    "decay_steps": 5000,
    "start_lr": 0.0001,
    "stop_lr": 3e-06
  },
  "loss": {
    "type": "ener",
    "start_pref_e": 0.2,
    "limit_pref_e": 20,
    "start_pref_f": 100,
    "limit_pref_f": 60,
    "start_pref_v": 0.02,
    "limit_pref_v": 1
  },
  "optimizer": {
    "type": "AdamW",
    "weight_decay": 0.001
  },
  "training": {
    "training_data": {
      "systems": [
        "./my_data/train_0",
        "./my_data/train_1"
      ],
      "batch_size": 1
    },
    "validation_data": {
      "systems": [
        "./my_data/valid_0"
      ],
      "batch_size": 1
    },
    "numb_steps": 200000,
    "gradient_max_norm": 5.0,
    "seed": 10,
    "disp_file": "lcurve.out",
    "disp_freq": 100,
    "save_freq": 2000
  }
}

The meaning of each parameter can be generated through dp doc-train-input. Considering the output RST documentation on the screen is very long, use grep to find the documentation of a specific parameter:

sh
dp doc-train-input | grep -A 7 training/numb_steps

Run fine-tuning:

bash
# Single-task fine-tuning from a specific branch
dp --pt train input.json --finetune /path/to/DPA-3.1-3M.pt --model-branch CHOSEN_BRANCH --use-pretrain-script

# If the pretrained model is single-task, --model-branch is not needed
dp --pt train input.json --finetune /path/to/DPA3-Omol-Large.pt --use-pretrain-script
Step 5: Freeze and Test
bash
dp --pt freeze -o finetuned_model.pth
dp --pt test -m finetuned_model.pth -s /path/to/test_system -n 30

Fine-tuning Command Reference

CommandDescription
dp pretrained download <MODEL>Download a built-in pretrained model
dp pretrained download <MODEL> --cache-dir <PATH>Download to a custom directory
dp --pt train input.json --finetune <MODEL>.ptFine-tune from a pre-trained model
dp --pt train input.json --finetune <MODEL>.pt --use-pretrain-scriptInherit model architecture from pre-trained model
dp --pt train input.json --finetune <MODEL>.pt --model-branch <BRANCH>Fine-tune from a specific branch
dp --pt train input.json --finetune <MODEL>.pt --model-branch RANDOMFine-tune with random fitting net
dp --pt show <MODEL>.pt model-branchList available branches in a multi-task model
dp --pt freeze -o model.pthFreeze the fine-tuned model
dp --pt freeze -o model.pth --head <BRANCH>Freeze a specific branch (multi-task)

Agent Checklist

  • Pre-trained model file exists (downloaded or self-trained)
  • Downstream data elements are a subset of the pre-trained model's type_map
  • --use-pretrain-script is used if model architecture is unknown
  • Learning rate is reduced compared to training from scratch (e.g., 1e-4 vs 1e-3)
  • For multi-task pretrained models, the correct --model-branch is selected
  • Training completes without NaN in lcurve.out
  • Fine-tuned model is frozen and tested
  • Test RMSE values are reported to the user

References

© jinzhezenggroup, LGPL-3.0-or-later. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in machine-learning-potentials/deepmd-finetune-dpa3 of jinzhezenggroup/computational-chemistry-agent-skills.

Open the folder on GitHubat commit 5c19e75

Compare with similar skills

Deepmd Finetune Dpa3 next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Works with

Categories

Questions about Deepmd Finetune Dpa3

What does Deepmd Finetune Dpa3 do?

Fine-tune a DPA3 model in DeePMD-kit using the PyTorch backend. Deepmd Finetune Dpa3 is an agent skill from jinzhezenggroup/computational-chemistry-agent-skills. Fine-tune a DPA3 model in DeePMD-kit using the PyTorch backend.

When should I use Deepmd Finetune Dpa3?

Deepmd Finetune Dpa3 fits situations like: the user wants to adapt a pre-trained DPA3 model to a new downstream dataset; tasks that involve Fine-tuning.

How do I install Deepmd Finetune Dpa3 in Claude Code?

Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-finetune-dpa3 -a claude-code`. Or copy the skill folder (machine-learning-potentials/deepmd-finetune-dpa3 in jinzhezenggroup/computational-chemistry-agent-skills) into .claude/skills/deepmd-finetune-dpa3 in your project. Claude Code loads it when a task matches its description.

How do I install Deepmd Finetune Dpa3 in Codex?

Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-finetune-dpa3 -a codex`. Or copy the skill folder (machine-learning-potentials/deepmd-finetune-dpa3 in jinzhezenggroup/computational-chemistry-agent-skills) into .agents/skills/deepmd-finetune-dpa3 in your project. Codex loads it when a task matches its description.

Can I use Deepmd Finetune Dpa3 in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill deepmd-finetune-dpa3 -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deepmd-finetune-dpa3, .gemini/skills/deepmd-finetune-dpa3, .github/skills/deepmd-finetune-dpa3 and .opencode/skills/deepmd-finetune-dpa3 in your project.

What does Deepmd Finetune Dpa3 need to run?

SKILL.md names no scripts, command-line tools or credentials: Deepmd Finetune Dpa3 is instructions for the agent only. Compatibility (from SKILL.md): Requires deepmd-kit with PyTorch backend installed. GPU strongly recommended..

Does Deepmd Finetune Dpa3 access the network?

SKILL.md names 2 domains. As links in the text: docs.deepmodeling.com and github.com. This is read from the text; nothing was executed.

Is Deepmd Finetune Dpa3 safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Deepmd Finetune Dpa3 use?

Deepmd Finetune Dpa3 is published under the LGPL-3.0-or-later licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deepmd Finetune Dpa3 use?

About 3.1k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Deepmd Finetune Dpa3?

Skills that share tags, products or a category with Deepmd Finetune Dpa3: ML Experiment Iteration (Leeroo-AI/superml, 195 stars), ML Training Run Verifier (Leeroo-AI/superml, 195 stars), nanoGPT Training Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars) and ML Training Recipes (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deepmd Finetune Dpa3?

jinzhezenggroup (a GitHub organization) maintains it in jinzhezenggroup/computational-chemistry-agent-skills, which has 148 GitHub stars. The repository holds 62 skills in this directory. The repository was last updated on October 9, 2026.

Source: jinzhezenggroup/computational-chemistry-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.