> ## Documentation Index
> Fetch the complete documentation index at: https://forge-64364c0e.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Inspect

> Deep analysis of model architecture, memory layout, capabilities, tensor composition, and runtime compatibility

# Inspect (02)

Inspect provides deep analysis of a loaded model — 3D architecture visualization, memory breakdown, capability detection, quantization distribution, runtime compatibility, and more.

<img src="https://mintcdn.com/forge-64364c0e/ndaDrHy7hzGW88bh/images/light/inspect-light.png?fit=max&auto=format&n=ndaDrHy7hzGW88bh&q=85&s=22402272556205b8d85ecd8678db2ed3" alt="Inspect Module" width="3440" height="1406" data-path="images/light/inspect-light.png" />

<Note>
  A model must be loaded via the Load module before Inspect is available.
</Note>

## Isometric 3D Visualization

An interactive isometric view renders the model as stacked blocks:

* **Embedding layer** at the bottom
* **Transformer layers** stacked vertically, colored by attention/MLP tensor ratio
* **Output layer** at the top

| Control           | Action                        |
| ----------------- | ----------------------------- |
| Hover             | Tooltip with tensor breakdown |
| `+` / `-` buttons | Zoom in / out                 |
| Mouse wheel       | Zoom                          |
| Reset button      | Reset view                    |

## Memory Distribution

Six-component breakdown showing how memory is allocated:

| Component  | Description                        |
| ---------- | ---------------------------------- |
| Embeddings | Token embedding weights            |
| Attention  | Q, K, V, O projection matrices     |
| MLP        | Gate, up, down projection matrices |
| Norms      | RMSNorm / LayerNorm weights        |
| Output     | LM head / output projection        |
| Other      | Miscellaneous tensors              |

Each component shows exact byte count, percentage, and a proportional bar.

## Quantization Breakdown

For each dtype present (F32, F16, BF16, Q8\_0, Q4\_K\_M, etc.):

* Tensor count, total size, percentage, and visual bar chart

## Capability Detection

<img src="https://mintcdn.com/forge-64364c0e/ndaDrHy7hzGW88bh/images/light/inspect-caps-light.png?fit=max&auto=format&n=ndaDrHy7hzGW88bh&q=85&s=1712a5e442a557b2a22751818a7f2a26" alt="Inspect Capabilities" width="3440" height="1406" data-path="images/light/inspect-caps-light.png" />

Analyzes model architecture to detect 7 capabilities with confidence scores:

| Capability       | What It Detects               |
| ---------------- | ----------------------------- |
| **Tool Calling** | API/function calling ability  |
| **Reasoning**    | Chain-of-thought reasoning    |
| **Code**         | Code generation/understanding |
| **Mathematics**  | Mathematical reasoning        |
| **Multilingual** | Multi-language support        |
| **Instruction**  | Instruction following         |
| **Safety**       | Safety/alignment layers       |

## Runtime Compatibility Matrix

Checks support across 8 popular inference runtimes:

| Runtime               | Formats           |
| --------------------- | ----------------- |
| llama.cpp             | GGUF              |
| Ollama                | GGUF              |
| LM Studio             | GGUF              |
| KoboldCpp             | GGUF              |
| GPT4All               | GGUF              |
| Jan                   | GGUF              |
| LocalAI               | GGUF              |
| text-generation-webui | GGUF, SafeTensors |

Status: COMPATIBLE, PARTIAL, or NOT SUPPORTED.

## Attention Architecture

* Query heads and KV heads count
* Head dimension and GQA ratio
* Visual head diagram

## Tokenizer Info

* Tokenizer type (BPE, Unigram, WordPiece)
* Vocabulary size
* Special tokens (BOS, EOS, PAD, UNK) with IDs

## File Verification

Click **COMPUTE HASH** to calculate SHA-256 fingerprint for integrity verification.

## Data Export

* **JSON** — Full model metadata as JSON
* **CSV** — Tensor list with names, dtypes, shapes, and sizes

## Layer Hierarchy

Expandable list per layer showing attention, MLP, norm, and other tensors. Filter by name, dtype, or layer range.

## Tensor Browser

Searchable, filterable list of all tensors with:

* Tensor name
* Data type
* Shape dimensions
* Memory size