Implementation:Ggml org Llama cpp Android GgufMetadata
| Knowledge Sources | |
|---|---|
| Domains | Android, GGUF |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Data class hierarchy representing structured metadata extracted from GGUF model files, covering all major metadata categories.
Description
The top-level `GgufMetadata` data class contains file-level fields (version, tensor count, KV count) and nested data classes for: `BasicInfo` (name, UUID, size label), `AuthorInfo` (organization, author, license, URLs), `AdditionalInfo` (type, description, tags, languages), `ArchitectureInfo` (architecture name, file type, vocab size, finetune), `BaseModelInfo` (for derivative models), `TokenizerInfo` (model type, special token IDs, chat template), `DimensionsInfo`, `AttentionInfo`, `RopeInfo`, and `ExpertsInfo`. Includes a `GgufVersion` enum supporting Legacy v1, Extended v2, and Validated v3 formats.
Usage
Use this type-safe Kotlin representation of GGUF file metadata to enable the Android app to display detailed model information (architecture, quantization, tokenizer configuration, licensing) to users before and during inference.
Code Reference
Source Location
- Repository: Ggml_org_Llama_cpp
- File: examples/llama.android/lib/src/main/java/com/arm/aichat/gguf/GgufMetadata.kt
- Lines: 1-132
Signature
data class GgufMetadata(
val version: GgufVersion,
val tensorCount: Long,
val kvCount: Long,
val basic: BasicInfo,
val author: AuthorInfo? = null,
val additional: AdditionalInfo? = null,
val architecture: ArchitectureInfo? = null,
val baseModels: List<BaseModelInfo>? = null,
val tokenizer: TokenizerInfo? = null,
val dimensions: DimensionsInfo? = null,
val attention: AttentionInfo? = null,
val rope: RopeInfo? = null,
val experts: ExpertsInfo? = null
)
enum class GgufVersion(val code: Int, val label: String) {
LEGACY_V1(1, "Legacy v1"),
EXTENDED_V2(2, "Extended v2"),
VALIDATED_V3(3, "Validated v3")
}
data class BasicInfo(val uuid: String?, val name: String?, val nameLabel: String?, val sizeLabel: String?)
data class AuthorInfo(val organization: String?, val author: String?, val doi: String?, val url: String?, val repoUrl: String?, val license: String?, val licenseLink: String?)
data class AdditionalInfo(val type: String?, val description: String?, val tags: List<String>?, val languages: List<String>?)
data class ArchitectureInfo(val architecture: String?, val fileType: Int?, val vocabSize: Int?, val finetune: String?, val quantizationVersion: Int?)
data class BaseModelInfo(val name: String?, val author: String?, val version: String?, val organization: String?, val description: String?, val repoUrl: String?, val doi: String?)
data class TokenizerInfo(val model: String?, val bosTokenId: Int?, val eosTokenId: Int?, val chatTemplate: String?)
data class DimensionsInfo(val embeddingLength: Int?, val feedForwardLength: Int?, val blockCount: Int?, val contextLength: Int?)
data class AttentionInfo(val headCount: Int?, val headCountKV: Int?, val layerNormRmsEpsilon: Float?)
data class RopeInfo(val dimensionCount: Int?, val freqBase: Float?, val scalingType: String?)
data class ExpertsInfo(val count: Int?, val usedCount: Int?)
Import
import java.io.IOException
I/O Contract
Inputs
| Name | Type | Required | Description |
|---|---|---|---|
| version | GgufVersion | Yes | GGUF file format version (v1, v2, or v3) |
| tensorCount | Long | Yes | Number of tensors in the GGUF file |
| kvCount | Long | Yes | Number of key-value metadata pairs |
| basic | BasicInfo | Yes | Basic model identification info (name, UUID, size label) |
Outputs
| Name | Type | Description |
|---|---|---|
| GgufMetadata | data class | Immutable structured representation of all GGUF metadata fields |
Usage Examples
// Access metadata fields after reading a GGUF file
val metadata: GgufMetadata = reader.readStructuredMetadata(inputStream)
// Display model information
println("Model: ${metadata.basic.name}")
println("Version: ${metadata.version}")
println("Architecture: ${metadata.architecture?.architecture}")
println("Vocab size: ${metadata.architecture?.vocabSize}")
println("Tensor count: ${metadata.tensorCount}")