prepare-model-upload

name	prepare-model-upload
description	Upload HuggingFace models from Colab to RunPod Network Volume.
metadata	{"author":"pokutuna","compatibility":"Google Colab, RunPod Network Volume with S3 API"}

Colab → RunPod Network Volume Model Upload

Downloading large models during GPU instance runtime wastes billing time. Use Colab to download from HuggingFace and upload to RunPod Network Volume via S3-compatible API.

Prerequisites

Colab Secrets

• HF_TOKEN: HuggingFace access token
• RUNPOD_STORAGE_ACCESS_KEY_ID: RunPod Storage Access Key ID
• RUNPOD_STORAGE_SECRET_ACCESS_KEY: RunPod Storage Secret Access Key

RunPod Storage Settings

Get from https://console.runpod.io/user/storage "S3 Compatible API Commands" Example:

aws s3 ls --region xxx --endpoint-url https://s3api-xxx.runpod.io s3://your-volume-id/

Steps

1. Open Colab notebook: https://colab.research.google.com/github/pokutuna/claude-plugins/blob/main/runpod/skills/prepare-model-upload/hf-to-runpod-storage.ipynb
2. Configure Colab Secrets (🔑 icon in left sidebar)
3. Enter model names and volume settings, then run

Notes

Sync Limitations

aws s3 sync may fail on large volumes:

fatal error: Error during pagination: The same next token was received twice: ...

Use aws s3 cp --recursive instead (no delta transfer).

Upload Failures

If upload failed occurs, retry with --checksum-algorithm=CRC32C.

Notebook Structure

The notebook has separate cells:

1. Environment Setup - Loads secrets from Colab (run once)
2. Settings - Model names and storage settings (user configures this)
3. Download and Upload - Main execution
4. Troubleshooting cells - For retry scenarios

Response Instructions

When user provides model names or aws cli command examples, output code snippets that can be directly copy-pasted into Colab cells.

When user provides model name(s)

Output a ready-to-paste Settings cell:

# Settings

# HuggingFace models (USER/REPOSITORY format, multiple allowed)
HF_MODELS = [
    "USER_PROVIDED_MODEL",  # parsed from user input
]

# RunPod Storage (copy from https://console.runpod.io/user/storage)
REGION = ""  # @param {type:"string"}
ENDPOINT_URL = ""  # @param {type:"string"}
BUCKET = ""  # @param {type:"string"}

When user provides aws cli command example

Parse the command and output a ready-to-paste Settings cell with values filled:

Example input:

aws s3 ls --region us-east-1 --endpoint-url https://s3api-xxxxxx.runpod.io s3://abc123def456/

Output:

# Settings

# HuggingFace models (USER/REPOSITORY format, multiple allowed)
HF_MODELS = [
    "",  # Add your model here, e.g., "Qwen/Qwen3-8B"
]

# RunPod Storage (parsed from aws cli command)
REGION = "us-east-1"  # @param {type:"string"}
ENDPOINT_URL = "https://s3api-xxxxxx.runpod.io"  # @param {type:"string"}
BUCKET = "abc123def456"  # @param {type:"string"}

When user provides both model name(s) and aws cli command

Combine both into a complete Settings cell ready to run.

Examples

User: "I want to put Qwen3-8B on my runpod network volume"

1. Provide Colab notebook URL
2. Guide Colab Secrets setup
3. Output Settings cell with model name filled:

   HF_MODELS = [
       "Qwen/Qwen3-8B",
   ]
   

User: "aws s3 ls --region us-east-1 --endpoint-url https://s3api-xxx.runpod.io s3://my-bucket/"

Parse and output Settings cell with storage values filled.

References

• runpod/runpod-s3-examples
• RunPod S3 API Known Issues