Hugging Face's logo

Buckets:
gijl
/
lv-storage

Files
xet
gijl/lv-storage / llama.cpp /tools /ui /tests /unit /model-id-parser.test.ts
gijl's picture
gijl
download
raw
13 kB
import type { ModelSidecar } from '$lib/constants';
import { ModelAuxSidecar, ModelDraftSidecar } from '$lib/enums';
import { ModelsService } from '$lib/services/models.service';
import { describe, expect, it } from 'vitest';
const { parseModelId } = ModelsService;
describe('parseModelId', () => {
it('handles unknown patterns correctly', () => {
expect(parseModelId('model-name-1')).toStrictEqual({
activatedParams: null,
modelName: 'model-name-1',
orgName: null,
params: null,
quantization: null,
raw: 'model-name-1',
sidecar: null,
tags: []
});
expect(parseModelId('org/model-name-2')).toStrictEqual({
activatedParams: null,
modelName: 'model-name-2',
orgName: 'org',
params: null,
quantization: null,
raw: 'org/model-name-2',
sidecar: null,
tags: []
});
});
it('extracts model parameters correctly', () => {
expect(parseModelId('model-100B-BF16')).toMatchObject({ params: '100B' });
expect(parseModelId('model-100B:Q4_K_M')).toMatchObject({ params: '100B' });
});
it('extracts model parameters correctly in lowercase', () => {
expect(parseModelId('model-100b-bf16')).toMatchObject({ params: '100B' });
expect(parseModelId('model-100b:q4_k_m')).toMatchObject({ params: '100B' });
});
it('extracts effective parameters correctly', () => {
expect(parseModelId('model-E4B-BF16')).toMatchObject({ params: 'E4B' });
expect(parseModelId('model-e2b:q4_k_m')).toMatchObject({ params: 'E2B' });
});
it('extracts activated parameters correctly', () => {
expect(parseModelId('model-100B-A10B-BF16')).toMatchObject({ activatedParams: 'A10B' });
expect(parseModelId('model-100B-A10B:Q4_K_M')).toMatchObject({ activatedParams: 'A10B' });
});
it('extracts activated parameters correctly in lowercase', () => {
expect(parseModelId('model-100b-a10b-bf16')).toMatchObject({ activatedParams: 'A10B' });
expect(parseModelId('model-100b-a10b:q4_k_m')).toMatchObject({ activatedParams: 'A10B' });
});
it('extracts quantization correctly', () => {
// Dash-separated quantization
expect(parseModelId('model-100B-UD-IQ1_S')).toMatchObject({ quantization: 'UD-IQ1_S' });
expect(parseModelId('model-100B-IQ4_XS')).toMatchObject({ quantization: 'IQ4_XS' });
expect(parseModelId('model-100B-Q4_K_M')).toMatchObject({ quantization: 'Q4_K_M' });
expect(parseModelId('model-100B-Q8_0')).toMatchObject({ quantization: 'Q8_0' });
expect(parseModelId('model-100B-UD-Q8_K_XL')).toMatchObject({ quantization: 'UD-Q8_K_XL' });
expect(parseModelId('model-100B-F16')).toMatchObject({ quantization: 'F16' });
expect(parseModelId('model-100B-BF16')).toMatchObject({ quantization: 'BF16' });
expect(parseModelId('model-100B-MXFP4')).toMatchObject({ quantization: 'MXFP4' });
// Colon-separated quantization
expect(parseModelId('model-100B:UD-IQ1_S')).toMatchObject({ quantization: 'UD-IQ1_S' });
expect(parseModelId('model-100B:IQ4_XS')).toMatchObject({ quantization: 'IQ4_XS' });
expect(parseModelId('model-100B:Q4_K_M')).toMatchObject({ quantization: 'Q4_K_M' });
expect(parseModelId('model-100B:Q8_0')).toMatchObject({ quantization: 'Q8_0' });
expect(parseModelId('model-100B:UD-Q8_K_XL')).toMatchObject({ quantization: 'UD-Q8_K_XL' });
expect(parseModelId('model-100B:F16')).toMatchObject({ quantization: 'F16' });
expect(parseModelId('model-100B:BF16')).toMatchObject({ quantization: 'BF16' });
expect(parseModelId('model-100B:MXFP4')).toMatchObject({ quantization: 'MXFP4' });
// Dot-separated quantization
expect(parseModelId('nomic-embed-text-v2-moe.Q4_K_M')).toMatchObject({
quantization: 'Q4_K_M'
});
});
it('extracts additional tags correctly', () => {
expect(parseModelId('model-100B-foobar-Q4_K_M')).toMatchObject({ tags: ['foobar'] });
expect(parseModelId('model-100B-A10B-foobar-1M-BF16')).toMatchObject({
tags: ['foobar', '1M']
});
expect(parseModelId('model-100B-1M-foobar:UD-Q8_K_XL')).toMatchObject({
tags: ['1M', 'foobar']
});
});
it('filters out container format segments from tags', () => {
expect(parseModelId('model-100B-GGUF-Instruct-BF16')).toMatchObject({
tags: ['Instruct']
});
expect(parseModelId('model-100B-GGML-Instruct:Q4_K_M')).toMatchObject({
tags: ['Instruct']
});
});
it('splits a download tag into quant and sidecar', () => {
expect(parseModelId('ggml-org/Qwen3.8-27B-GGUF:Q8_0-dflash')).toMatchObject({
orgName: 'ggml-org',
params: '27B',
quantization: 'Q8_0',
sidecar: 'dflash'
});
expect(parseModelId('ggml-org/Qwen3.8-27B-GGUF:Q4_K_M-mtp')).toMatchObject({
quantization: 'Q4_K_M',
sidecar: 'mtp'
});
// a bare sidecar tag is the sidecar alone, with no quant
expect(parseModelId('ggml-org/Qwen3.8-27B-GGUF:mmproj')).toMatchObject({
quantization: null,
sidecar: 'mmproj'
});
// a plain quant tag stays a quant
expect(parseModelId('unsloth/DeepSeek-V4-Flash-0731-GGUF:UD-Q2_K_XL')).toMatchObject({
quantization: 'UD-Q2_K_XL',
sidecar: null
});
});
it('strips trailing container format segments from model names', () => {
expect(parseModelId('unsloth/DeepSeek-V4-Flash-0731-GGUF:Q2_K_XL')).toStrictEqual({
activatedParams: null,
modelName: 'DeepSeek-V4-Flash-0731',
orgName: 'unsloth',
params: null,
quantization: 'Q2_K_XL',
raw: 'unsloth/DeepSeek-V4-Flash-0731-GGUF:Q2_K_XL',
sidecar: null,
tags: []
});
expect(parseModelId('unsloth/Laguna-S-2.1-GGUF:Q4_K_XL')).toStrictEqual({
activatedParams: null,
modelName: 'Laguna-S-2.1',
orgName: 'unsloth',
params: null,
quantization: 'Q4_K_XL',
raw: 'unsloth/Laguna-S-2.1-GGUF:Q4_K_XL',
sidecar: null,
tags: []
});
expect(parseModelId('org/Model-Name-GGUF')).toStrictEqual({
activatedParams: null,
modelName: 'Model-Name',
orgName: 'org',
params: null,
quantization: null,
raw: 'org/Model-Name-GGUF',
sidecar: null,
tags: []
});
});
it('handles real-world examples correctly', () => {
expect(parseModelId('meta-llama/Llama-3.1-8B')).toStrictEqual({
activatedParams: null,
modelName: 'Llama-3.1',
orgName: 'meta-llama',
params: '8B',
quantization: null,
raw: 'meta-llama/Llama-3.1-8B',
sidecar: null,
tags: []
});
expect(parseModelId('openai/gpt-oss-120b-MXFP4')).toStrictEqual({
activatedParams: null,
modelName: 'gpt-oss',
orgName: 'openai',
params: '120B',
quantization: 'MXFP4',
raw: 'openai/gpt-oss-120b-MXFP4',
sidecar: null,
tags: []
});
expect(parseModelId('openai/gpt-oss-20b:Q4_K_M')).toStrictEqual({
activatedParams: null,
modelName: 'gpt-oss',
orgName: 'openai',
params: '20B',
quantization: 'Q4_K_M',
raw: 'openai/gpt-oss-20b:Q4_K_M',
sidecar: null,
tags: []
});
expect(parseModelId('Qwen/Qwen3-Coder-30B-A3B-Instruct-1M-BF16')).toStrictEqual({
activatedParams: 'A3B',
modelName: 'Qwen3-Coder',
orgName: 'Qwen',
params: '30B',
quantization: 'BF16',
raw: 'Qwen/Qwen3-Coder-30B-A3B-Instruct-1M-BF16',
sidecar: null,
tags: ['Instruct', '1M']
});
});
it('handles real-world examples with quantization in segments', () => {
expect(parseModelId('meta-llama/Llama-4-Scout-17B-16E-Instruct-Q4_K_M')).toStrictEqual({
activatedParams: null,
modelName: 'Llama-4-Scout',
orgName: 'meta-llama',
params: '17B',
quantization: 'Q4_K_M',
raw: 'meta-llama/Llama-4-Scout-17B-16E-Instruct-Q4_K_M',
sidecar: null,
tags: ['16E', 'Instruct']
});
expect(parseModelId('MiniMaxAI/MiniMax-M2-IQ4_XS')).toStrictEqual({
activatedParams: null,
modelName: 'MiniMax-M2',
orgName: 'MiniMaxAI',
params: null,
quantization: 'IQ4_XS',
raw: 'MiniMaxAI/MiniMax-M2-IQ4_XS',
sidecar: null,
tags: []
});
expect(parseModelId('MiniMaxAI/MiniMax-M2-UD-Q3_K_XL')).toStrictEqual({
activatedParams: null,
modelName: 'MiniMax-M2',
orgName: 'MiniMaxAI',
params: null,
quantization: 'UD-Q3_K_XL',
raw: 'MiniMaxAI/MiniMax-M2-UD-Q3_K_XL',
sidecar: null,
tags: []
});
expect(parseModelId('mistralai/Devstral-2-123B-Instruct-2512-Q4_K_M')).toStrictEqual({
activatedParams: null,
modelName: 'Devstral-2',
orgName: 'mistralai',
params: '123B',
quantization: 'Q4_K_M',
raw: 'mistralai/Devstral-2-123B-Instruct-2512-Q4_K_M',
sidecar: null,
tags: ['Instruct', '2512']
});
expect(parseModelId('mistralai/Devstral-Small-2-24B-Instruct-2512-Q8_0')).toStrictEqual({
activatedParams: null,
modelName: 'Devstral-Small-2',
orgName: 'mistralai',
params: '24B',
quantization: 'Q8_0',
raw: 'mistralai/Devstral-Small-2-24B-Instruct-2512-Q8_0',
sidecar: null,
tags: ['Instruct', '2512']
});
expect(parseModelId('noctrex/GLM-4.7-Flash-MXFP4_MOE')).toStrictEqual({
activatedParams: null,
modelName: 'GLM-4.7-Flash',
orgName: 'noctrex',
params: null,
quantization: 'MXFP4_MOE',
raw: 'noctrex/GLM-4.7-Flash-MXFP4_MOE',
sidecar: null,
tags: []
});
expect(parseModelId('Qwen/Qwen3-Coder-Next-Q4_K_M')).toStrictEqual({
activatedParams: null,
modelName: 'Qwen3-Coder-Next',
orgName: 'Qwen',
params: null,
quantization: 'Q4_K_M',
raw: 'Qwen/Qwen3-Coder-Next-Q4_K_M',
sidecar: null,
tags: []
});
expect(parseModelId('openai/gpt-oss-120b-Q4_K_M')).toStrictEqual({
activatedParams: null,
modelName: 'gpt-oss',
orgName: 'openai',
params: '120B',
quantization: 'Q4_K_M',
raw: 'openai/gpt-oss-120b-Q4_K_M',
sidecar: null,
tags: []
});
expect(parseModelId('openai/gpt-oss-20b-F16')).toStrictEqual({
activatedParams: null,
modelName: 'gpt-oss',
orgName: 'openai',
params: '20B',
quantization: 'F16',
raw: 'openai/gpt-oss-20b-F16',
sidecar: null,
tags: []
});
expect(parseModelId('nomic-embed-text-v2-moe.Q4_K_M')).toStrictEqual({
activatedParams: null,
modelName: 'nomic-embed-text-v2-moe',
orgName: null,
params: null,
quantization: 'Q4_K_M',
raw: 'nomic-embed-text-v2-moe.Q4_K_M',
sidecar: null,
tags: []
});
});
it('handles ambiguous model names', () => {
// Qwen3.5 Instruct vs Thinking — tags should distinguish them
expect(parseModelId('Qwen/Qwen3.5-30B-A3B-Instruct')).toMatchObject({
activatedParams: 'A3B',
modelName: 'Qwen3.5',
params: '30B',
tags: ['Instruct']
});
expect(parseModelId('Qwen/Qwen3.5-30B-A3B-Thinking')).toMatchObject({
activatedParams: 'A3B',
modelName: 'Qwen3.5',
params: '30B',
tags: ['Thinking']
});
// Dot-separated quantization with variant suffixes
expect(parseModelId('gemma-3-27b-it-heretic-v2.Q8_0')).toMatchObject({
modelName: 'gemma-3',
params: '27B',
quantization: 'Q8_0',
tags: ['it', 'heretic', 'v2']
});
expect(parseModelId('gemma-3-27b-it.Q8_0')).toMatchObject({
modelName: 'gemma-3',
params: '27B',
quantization: 'Q8_0',
tags: ['it']
});
});
it('parses sidecar file tokens', () => {
// sidecar prefix: bare filename or multi-slash path reduces to the filename
expect(parseModelId('mtp-Q4_0.gguf')).toMatchObject({
quantization: 'Q4_0',
sidecar: ModelDraftSidecar.MTP
});
expect(parseModelId('ggml-org/Model-GGUF/mtp-Q4_0.gguf')).toMatchObject({
quantization: 'Q4_0',
sidecar: ModelDraftSidecar.MTP
});
expect(parseModelId('ggml-org/Model-GGUF/mmproj-F16.gguf')).toMatchObject({
quantization: 'F16',
sidecar: ModelAuxSidecar.MMPROJ
});
// embedded-draft suffix: -<type> only strips when preceded by a quant
expect(parseModelId('ggml-org/Hy3-IQ1_M-mtp')).toMatchObject({
modelName: 'Hy3',
quantization: 'IQ1_M',
sidecar: ModelDraftSidecar.MTP
});
// a model literally named MyModel-mtp is not a draft
expect(parseModelId('ggml-org/MyModel-mtp')).toMatchObject({
modelName: 'MyModel-mtp',
sidecar: null
});
// no sidecar
expect(parseModelId('ggml-org/model-Q4_K_M')).toMatchObject({
quantization: 'Q4_K_M',
sidecar: null
});
});
});
describe('draftSidecarsByRepo', () => {
const entry = (id: string, sidecars: ModelSidecar[], status = 'unloaded') => ({
id,
sidecars,
status: { value: status }
});
it('keeps one badge per kind, at the repo that has it on disk', () => {
const byRepo = ModelsService.draftSidecarsByRepo([
entry('ggml-org/model:Q4_0', [ModelDraftSidecar.MTP]),
entry('ggml-org/model:Q8_0', [ModelDraftSidecar.MTP])
]);
// the listing reports kinds, never the draft's file: its quant stays unknown
expect(byRepo.get('ggml-org/model')).toEqual([
{ kind: ModelDraftSidecar.MTP, quant: null, repo: 'ggml-org/model' }
]);
});
it('drops duplicates, aux sidecars and unfinished downloads', () => {
const byRepo = ModelsService.draftSidecarsByRepo([
entry('ggml-org/model:Q4_0', [ModelDraftSidecar.MTP, ModelAuxSidecar.MMPROJ]),
entry('ggml-org/model:Q4_0', [ModelDraftSidecar.MTP]),
entry('ggml-org/model:Q8_0', [ModelDraftSidecar.MTP], 'incomplete')
]);
expect(byRepo.get('ggml-org/model')).toEqual([
{ kind: ModelDraftSidecar.MTP, quant: null, repo: 'ggml-org/model' }
]);
});
});

Xet Storage Details

Size:
13 kB
·
Xet hash:
07ab0e3077deba2d504c616fcc69b92e454329fb31ed91af2b007d0cbe516338

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.