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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsTo use local AI models from a Python script without internet access, download each model and its tokenizer while connected, save them in separate local folders, and load the folder you want after disconnecting. With Hugging Face Transformers, set HF_HUB_OFFLINE=1 and pass local_files_only=True so model loading does not contact the Hub. You must also prepare the Python environment and any runtime components before going off grid.
Prepare the model files while you are online
Downloading and running a model are separate steps. A model folder needs the files required to load both its weights and tokenizer, along with configuration files. The official Transformers v4.49.0 guide demonstrates downloading a model, saving it with save_pretrained, and loading it later from a local path: Transformers offline mode.
This illustrative example adapts that workflow for a causal language model. Choose an architecture supported by AutoModelForCausalLM and follow the model card’s requirements; the code has not been executed as a platform test.
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "organization/model-repository"
local_dir = "models/model-a"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer.save_pretrained(local_dir)
model.save_pretrained(local_dir)For full repository acquisition, the Hugging Face Hub CLI can download files to a chosen folder and select a revision. Inspect the planned transfer first with --dry-run; the documented CLI guide notes that it reports proposed downloads and approximate sizes. Confirm the syntax against your installed CLI version.
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hf download organization/model-repository
--revision <commit-or-tag>
--local-dir models/model-aThe --revision value may be a commit hash, branch, or tag. For reproducibility, use a known revision rather than relying on a moving default. The CLI guide also explains that local-directory metadata can avoid unnecessary repeat downloads when files are up to date: Hugging Face Hub CLI guide.
Load a prepared model while disconnected
Set offline mode before loading Transformers, then point both the tokenizer and model loader to the prepared directory. Setting both controls makes the intent explicit: the environment variable disables Hub HTTP calls, while local_files_only=True restricts these loads to files available locally.
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import os
os.environ["HF_HUB_OFFLINE"] = "1"
from transformers import AutoTokenizer, AutoModelForCausalLM
local_dir = "models/model-a"
tokenizer = AutoTokenizer.from_pretrained(
local_dir, local_files_only=True
)
model = AutoModelForCausalLM.from_pretrained(
local_dir, local_files_only=True
)The local path is the switch: use another prepared model directory to load a different model. Keep the tokenizer paired with its model, and check the model architecture and model-card instructions before choosing an automatic loader class.
Switch models by selecting a local directory
Store each prepared model in its own directory and select the directory through configuration. For example, change MODEL_DIR from models/model-a to models/model-b only after both folders contain their corresponding model and tokenizer files.
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import os
from transformers import AutoTokenizer, AutoModelForCausalLM
os.environ["HF_HUB_OFFLINE"] = "1"
MODEL_DIR = "models/model-b" # choose another prepared folder
tokenizer = AutoTokenizer.from_pretrained(
MODEL_DIR, local_files_only=True
)
model = AutoModelForCausalLM.from_pretrained(
MODEL_DIR, local_files_only=True
)This is path-based switching within a compatible Transformers workflow, not a guarantee that any two models are interchangeable. A different architecture may require another model class, and a different file format may require a different runtime.
Choose a runtime that supports your model and workflow
Transformers is one option, not a universal loader for every local model. Hugging Face’s local-apps documentation describes several approaches, including Transformers, llama.cpp, Ollama, Jan, and LM Studio: Hugging Face local apps.
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| Option | Python or local interface | What to check before going offline |
|---|---|---|
| Transformers | Load model and tokenizer from local paths in Python. | Model architecture, required files, package versions, and target hardware support. |
| llama.cpp | Offers CLI, server, and Python interfaces. | Whether the model format is supported and whether the selected build works on the target computer. |
| LM Studio | Provides a Python SDK and OpenAI-like local endpoints. | Acquire model files and required runtimes while online; confirm that the model and endpoint fit the application. |
| Ollama or Jan | Listed by Hugging Face among local-app options; specific integration details depend on the application. | Confirm model support, API or Python integration, and offline runtime setup in the chosen product’s documentation. |
These options are not a head-to-head performance ranking. Choose based on model format and architecture, operating system and CPU/GPU support, whether your script should load weights directly or call a local server, and how the runtime identifies models. The available documentation does not establish a universal best runtime or comparative benchmark.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What “off grid” requires beyond model weights
Offline inference works only if the complete software stack is already present. Stage and preserve the environment that works on the target machine; no single package stack or set of versions fits every model and platform.
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- Python packages and dependencies: Install Transformers or the chosen runtime and all required libraries before disconnecting.
- Runtime and system components: Prepare the runtime binaries and, where applicable, compatible GPU drivers and other system dependencies.
- Model access requirements: Resolve gated-model access and obtain the necessary files while connected; observe the model’s license and use terms.
- Storage and transfer: Estimate space from the specific model files and runtime you choose. An external SSD can help carry artifacts to an isolated computer, but it does not replace staging software dependencies.
- Revision and environment records: Keep the selected model revision and the package or runtime versions with the prepared files so you can reproduce the setup.
Storage needs vary by model. The Hub CLI documentation shows illustrative cache examples of 32.1G for a model entry and 35.5G for an aggregate cache; these are examples from the guide, not general estimates for all models.
LM Studio can run locally, but acquisition still needs connectivity
LM Studio’s official offline guidance says that using already downloaded LLMs, chatting, document chat, and running a local server do not require internet. Searching for models and downloading them do. The same page identifies runtime downloads and some catalog details as network-dependent: LM Studio offline use.
The page describes runtime hot-swapping as available “As of LM Studio 0.3.0.” Treat that as a capability documented for that version, not a promise about every future build. Check the documentation for the version you install and obtain the model and runtime files before isolating the machine.
Verify the setup before relying on it
Documentation describes the supported workflow, but it does not prove that a particular combination of model, packages, drivers, runtime, and hardware will work on your computer. Validate the exact target environment before taking it off grid.
- On a connected machine, download the chosen model and tokenizer or complete repository, using an explicit revision where possible.
- Install and record the Python packages, runtime, drivers, and other dependencies required by that model and target computer.
- Copy the model directories and environment artifacts to the offline machine, preserving the directory structure.
- Block network access and run the script with offline settings enabled. Confirm that the intended model loads and produces output without a Hub or runtime download request.
- Switch the configured path or runtime-specific model identifier, then repeat the check for every model you intend to use.
The Transformers offline instructions cited here are for version 4.49.0. Keep the library version and installation artifacts aligned with the environment you prepare, and consult the current documentation for later releases: Transformers v4.49.0 offline mode.
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