Add gemma

src/index.ts

@@ -5,6 +5,7 @@ import type {
   BaseGGUFMetadata,
   BloomMetadata,
   FalconMetadata,
+  GemmaMetadata,
   GGUFMetadata,
   GPT2Metadata,
   GPTJMetadata,
@@ -16,6 +17,7 @@ import type {
 import {
   bloomMetadataSchema,
   falconMetadataSchema,
+  gemmaMetadataSchema,
   gPT2MetadataSchema,
   gPTJMetadataSchema,
   gPTNeoXMetadataSchema,
@@ -263,6 +265,7 @@ const isValidArchitecture = (
     'gpt2',
     'bloom',
     'falcon',
+    'gemma',
     'rwkv',
   ].includes(architecture)
 }
@@ -317,6 +320,11 @@ const validateMetadata = (
       if (res.success === false) return { error: res.error }
       return { metadata: res.data }
     }
+    case 'gemma': {
+      const res = gemmaMetadataSchema.safeParse(metadata)
+      if (res.success === false) return { error: res.error }
+      return { metadata: res.data }
+    }
     case 'rwkv': {
       const res = rWKVMetadataSchema.safeParse(metadata)
       if (res.success === false) return { error: res.error }
@@ -604,6 +612,12 @@ export const isFalconMetadata = (
   return metadata.general.architecture === 'falcon'
 }
 
+export const isGemmaMetadata = (
+  metadata: GGUFMetadata,
+): metadata is GemmaMetadata => {
+  return metadata.general.architecture === 'gemma'
+}
+
 export const isRWKVMetadata = (
   metadata: GGUFMetadata,
 ): metadata is RWKVMetadata => {

src/metadataTypes.ts

@@ -10,6 +10,7 @@ export type ArchitectureType =
   | 'gpt2'
   | 'bloom'
   | 'falcon'
+  | 'gemma'
   | 'rwkv'
 
 export type BaseGGUFMetadata = {
@@ -369,6 +370,37 @@ export type RWKVMetadata = {
   }
 }
 
+export type GemmaMetadata = {
+  gemma: {
+    attention: {
+      /** Also known as n_head. Number of attention heads. */
+      head_count: number
+      /** The number of heads per group used in Grouped-Query-Attention. If not
+       * present or if present and equal to [llm].attention.head_count, the model
+       * does not use GQA. */
+      head_count_kv?: number
+      /** Layer RMS normalization epsilon. */
+      layer_norm_rms_epsilon: number
+    }
+    block_count: number
+    /** Length of the context used during training or fine-tuning. RWKV is able
+     * to handle larger context than this limit, but the output quality
+     * may suffer. */
+    context_length: number
+    /** Also known as n_embd. Embedding layer size. */
+    embedding_length: number
+    /** Also known as n_ff. The length of the feedforward layer. */
+    feed_forward_length: number
+  }
+  general: BaseGGUFMetadata & {
+    /**
+     * describes what architecture this model implements. All lowercase ASCII,
+     * with only [a-z0-9]+ characters allowed.
+     **/
+    architecture: 'gemma'
+  }
+}
+
 export type WhisperMetadata = {
   general: BaseGGUFMetadata & {
     /**
@@ -416,5 +448,6 @@ export type GGUFMetadata =
   | GPT2Metadata
   | BloomMetadata
   | FalconMetadata
+  | GemmaMetadata
   | RWKVMetadata
   | WhisperMetadata

src/zodValidators.ts

@@ -9,6 +9,7 @@ export const architectureTypeSchema = z.union([
   z.literal('gpt2'),
   z.literal('bloom'),
   z.literal('falcon'),
+  z.literal('gemma'),
   z.literal('rwkv'),
 ])
 
@@ -218,6 +219,25 @@ export const rWKVMetadataSchema = z.object({
   }),
 })
 
+export const gemmaMetadataSchema = z.object({
+  gemma: z.object({
+    attention: z.object({
+      head_count: z.number(),
+      head_count_kv: z.number().optional(),
+      layer_norm_rms_epsilon: z.number(),
+    }),
+    block_count: z.number(),
+    context_length: z.number(),
+    embedding_length: z.number(),
+    feed_forward_length: z.number(),
+  }),
+  general: baseGGUFMetadataSchema.and(
+    z.object({
+      architecture: z.literal('gemma'),
+    }),
+  ),
+})
+
 export const whisperMetadataSchema = z.object({
   general: baseGGUFMetadataSchema.and(
     z.object({
@@ -253,6 +273,7 @@ export const gGUFMetadataSchema = z.union([
   gPT2MetadataSchema,
   bloomMetadataSchema,
   falconMetadataSchema,
+  gemmaMetadataSchema,
   rWKVMetadataSchema,
   whisperMetadataSchema,
 ])