chore: add DPA-3.3-1M (deepmodeling#5489)

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **New Features**
* Added DPA-3.3-1M as a built-in pretrained model available for
automatic download and use in inference and fine-tuning workflows.

* **Documentation**
* Updated docs and skill guides to list DPA-3.3-1M among available
pretrained models.
* Added guidance and a download command example for obtaining and using
DPA-3.3-1M in inference and fine-tuning.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Anyang Peng <137014849+anyangml@users.noreply.github.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

Author: anyangml

deepmd/pretrained/registry.py

@@ -6,6 +6,15 @@
 )
 
 MODEL_REGISTRY: dict[str, dict[str, Any]] = {
+    "DPA-3.3-1M": {
+        "urls": [
+            "https://huggingface.co/deepmodelingcommunity/DPA-3.3-1M/resolve/main/DPA-3.3-1M.pt?download=true",
+            "https://hf-mirror.com/deepmodelingcommunity/DPA-3.3-1M/resolve/main/DPA-3.3-1M.pt?download=true",
+            "https://modelscope.cn/models/DeepModelingCommunity/DPA-3.3-1M/resolve/master/DPA-3.3-1M.pt",
+        ],
+        "filename": "DPA-3.3-1M.pt",
+        "sha256": "36fe440c111108d60cda54aa7d3fccac743794de25abef4d49564b9fb896a55b",
+    },
     "DPA-3.2-5M": {
         "urls": [
             "https://huggingface.co/deepmodelingcommunity/DPA-3.2-5M/resolve/main/DPA-3.2-5M.pt?download=true",

doc/model/pretrained.md

@@ -17,6 +17,7 @@ You can run `dp pretrained download -h` to see the currently supported model lis
 
 Examples in this release include:
 
+- `DPA-3.3-1M`
 - `DPA-3.2-5M`
 - `DPA-3.1-3M`
 - `DPA3-Omol-Large`

skills/deepmd-finetune-dpa3/SKILL.md

@@ -1,6 +1,6 @@
 ---
 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). Covers single-task and multi-task fine-tuning workflows.
+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:
@@ -15,7 +15,7 @@ Fine-tune a pre-trained DPA3 model on a downstream dataset. This skill covers th
 
 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) downloaded via `dp pretrained download`
+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
 
@@ -32,7 +32,7 @@ dp --pt train input.json --finetune /path/to/DPA-3.2-5M.pt --use-pretrain-script
 
 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, etc.)?
+   - 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.
@@ -279,6 +279,7 @@ 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

skills/deepmd-python-inference/SKILL.md

@@ -58,7 +58,7 @@ dp = DeepPot("graph.pb")
 dp = DeepPot("DPA-3.2-5M")
 ```
 
-Built-in pretrained model names include `DPA-3.2-5M`, `DPA-3.1-3M`, `DPA3-Omol-Large`, etc. DeePMD-kit will automatically download and cache the model on first use.
+Built-in pretrained model names include `DPA-3.3-1M`, `DPA-3.2-5M`, `DPA-3.1-3M`, `DPA3-Omol-Large`, etc. DeePMD-kit will automatically download and cache the model on first use.
 
 ### Predict Energy, Forces, and Virial
 
@@ -263,6 +263,7 @@ print(f"Forces:\n{f[0]}")
 To download pretrained models explicitly:
 
 ```bash
+dp pretrained download DPA-3.3-1M
 dp pretrained download DPA-3.2-5M
 dp pretrained download DPA-3.1-3M
 dp pretrained download DPA-3.2-5M --cache-dir ./models