Modal Gpu

OrganizationPopular

benchflow-ai

modal-gpu

Run Python code on cloud GPUs using Modal serverless platform. Use when you need A100/T4/A10G GPU access for training ML models. Covers Modal app setup, GPU selection, data downloading inside functions, and result handling.

Overview

Publisher	benchflow-ai
Repository	`skillsbench`
Skill name	`modal-gpu`
Stars	1.8K
Forks	367
Bundled files	5
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
5 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by benchflow-ai on GitHub. Read the source before you install it.

Installation

Install the Modal Gpu AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/diff-transformer_impl/environment/skills/modal-gpu

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git /tmp/skillsbench
mkdir -p .claude/skills
cp -r /tmp/skillsbench/tasks-extra/diff-transformer_impl/environment/skills/modal-gpu .claude/skills/modal-gpu

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Modal Gpu in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Modal Gpu on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Modal Gpu is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Modal GPU Training

Overview

Modal is a serverless platform for running Python code on cloud GPUs. It provides:

Serverless GPUs: On-demand access to T4, A10G, A100 GPUs
Container Images: Define dependencies declaratively with pip
Remote Execution: Run functions on cloud infrastructure
Result Handling: Return Python objects from remote functions

Two patterns:

Single Function: Simple script with @app.function decorator
Multi-Function: Complex workflows with multiple remote calls

Quick Reference

Topic	Reference
Basic Structure	Getting Started
GPU Options	GPU Selection
Data Handling	Data Download
Results & Outputs	Results
Troubleshooting	Common Issues

Installation

bash
pip install modal
modal token set --token-id <id> --token-secret <secret>

Minimal Example

python
import modal

app = modal.App("my-training-app")

image = modal.Image.debian_slim(python_version="3.11").pip_install(
    "torch",
    "einops",
    "numpy",
)

@app.function(gpu="A100", image=image, timeout=3600)
def train():
    import torch
    device = torch.device("cuda")
    print(f"Using GPU: {torch.cuda.get_device_name(0)}")

    # Training code here
    return {"loss": 0.5}

@app.local_entrypoint()
def main():
    results = train.remote()
    print(results)

Common Imports

python
import modal
from modal import Image, App

# Inside remote function
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download

When to Use What

Scenario	Approach
Quick GPU experiments	`gpu="T4"` (16GB, cheapest)
Medium training jobs	`gpu="A10G"` (24GB)
Large-scale training	`gpu="A100"` (40/80GB, fastest)
Long-running jobs	Set `timeout=3600` or higher
Data from HuggingFace	Download inside function with `hf_hub_download`
Return metrics	Return dict from function

Running

bash
# Run script
modal run train_modal.py

# Run in background
modal run --detach train_modal.py

External Resources

Modal Documentation: https://modal.com/docs
Modal Examples: https://github.com/modal-labs/modal-examples

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Modal Gpu AI skill do?

Why use Modal Gpu on TypingMind?

Because you install it once and use it with any model. Modal Gpu is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Modal Gpu in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/benchflow-ai/skillsbench/tree/main/tasks-extra/diff-transformer_impl/environment/skills/modal-gpu. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Modal Gpu?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Modal Gpu?

As many as you like. As long as a model supports skills, you can use Modal Gpu with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Modal Gpu AI skill free?

Yes. It is published on GitHub by benchflow-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Modal GPU Training

Overview

Quick Reference

Installation

Minimal Example

Common Imports

When to Use What

Running

External Resources

Bundled files

Frequently asked questions

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