> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tinfoil.sh/llms.txt
> Use this file to discover all available pages before exploring further.

# Model weights

> Prepare Hugging Face model weights for a Tinfoil Container.

## Overview

The **Models** tab prepares Hugging Face model weights for use inside a
Tinfoil Container. It does not deploy an inference server by itself. Instead,
it creates a verified model-weight artifact and gives you the `models:` block
to add to `tinfoil-config.yml`.

Use this when you are deploying a GPU inference container, such as vLLM, and
want the model weights to be pinned and verified separately from the Docker
image.

<Info>
  Your Docker image still needs to contain the inference server runtime. The
  Models tab prepares the weights that the runtime will load.
</Info>

## Why this exists

Enclave attestation proves what code and configuration were present when the
enclave booted. Model weights are usually loaded from disk after boot, so they
need their own integrity commitment. Tinfoil uses Modelwrap to turn a pinned
Hugging Face commit into a read-only model package with a dm-verity root hash.
The enclave config commits to that root hash, and dm-verity verifies each disk
read while the inference server loads the model. For the full technical
explanation, read
[How Tinfoil Proves Exactly What Model Is Running](https://tinfoil.sh/blog/2026-02-03-proving-model-identity).

## Prepare weights

1. Open the [Tinfoil Dashboard](https://dash.tinfoil.sh)
2. Go to **Tinfoil Containers** > **Models**
3. Enter the Hugging Face repo in `owner/model` form
4. Use the auto-filled commit, or paste a specific commit SHA
5. Add an HF token if the repo is gated or private
6. Click **Prepare weights**

Large models can take several minutes to wrap. When the job finishes, copy the
generated `models:` block into your config repo.

## Add the model block

The generated block looks like this:

```yaml tinfoil-config.yml theme={"dark"}
models:
  - name: "gemma-4-31b-it"
    repo: "google/gemma-4-31B-it@419b2efe421994fdfd3394e621983d4cc511cd4f"
    mpk: "0900ca6b913db0036792149d3ea5862986d66a6964b010e998f56fbb7e1276ab_62578683904_59fe9787-ed93-577a-9fd9-a7804c932a11"
```

| Field  | Type   | Required | Description                                                        |
| ------ | ------ | -------- | ------------------------------------------------------------------ |
| `name` | string | Yes      | Local identifier for the model artifact                            |
| `repo` | string | Yes      | Hugging Face repo pinned to a commit, in `owner/model@commit` form |
| `mpk`  | string | Yes      | Model package metadata generated by the Models tab                 |

The `mpk` value is generated by Tinfoil and includes the model root hash,
verity offset, and verity UUID. Keep it exactly as generated.

## Point your server at the mounted model

At boot, Tinfoil verifies the model artifact and mounts it read-only under
`/tinfoil/mpk`. In your inference server command, use:

```yaml tinfoil-config.yml theme={"dark"}
command: [
  "--model", "/tinfoil/mpk/mpk-0900ca6b913db0036792149d3ea5862986d66a6964b010e998f56fbb7e1276ab",
  "--served-model-name", "gemma-4-31b-it",
  "--port", "8001"
]
```

The path uses only the root hash portion of the `mpk` value:

```text theme={"dark"}
/tinfoil/mpk/mpk-<root_hash>
```

## Example vLLM config

```yaml tinfoil-config.yml theme={"dark"}
cvm-version: "<CVM_VERSION>" # TODO: Use latest version from https://github.com/tinfoilsh/cvmimage
cpus: 16
memory: 65536
gpus: 1

models:
  - name: "gemma-4-31b-it"
    repo: "google/gemma-4-31B-it@419b2efe421994fdfd3394e621983d4cc511cd4f"
    mpk: "0900ca6b913db0036792149d3ea5862986d66a6964b010e998f56fbb7e1276ab_62578683904_59fe9787-ed93-577a-9fd9-a7804c932a11"

containers:
  - name: "inference"
    image: "vllm/vllm-openai:v0.14.1@sha256:..."
    runtime: nvidia
    gpus: all
    ipc: host
    restart: always
    command: [
      "--model", "/tinfoil/mpk/mpk-0900ca6b913db0036792149d3ea5862986d66a6964b010e998f56fbb7e1276ab",
      "--served-model-name", "gemma-4-31b-it",
      "--port", "8001"
    ]
    healthcheck:
      test: ["CMD", "curl", "-sf", "http://localhost:8001/health"]
      interval: 30s
      timeout: 5s
      start_period: 30m

shim:
  upstream-port: 8001
  paths:
    - /v1/chat/completions
    - /v1/models
    - /health
```

After editing the config, commit it and trigger the **Tinfoil Release**
workflow with a new version. Wait for both phases to complete, then deploy or update
from the **All Containers** tab.

## Updating weights

To update a model, prepare the new Hugging Face commit from the **Models** tab,
replace the `repo` and `mpk` values in `tinfoil-config.yml`, then release a new
version via the **Tinfoil Release** workflow.

<Tip>
  Keep each deployment pinned to a specific Hugging Face commit. Avoid relying
  on a moving default branch for production workloads.
</Tip>