271 lines
8.4 KiB
TypeScript
271 lines
8.4 KiB
TypeScript
import { describe, expect, it } from 'vitest';
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import { ModelsService } from '$lib/services/models.service';
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const { parseModelId } = ModelsService;
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describe('parseModelId', () => {
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it('handles unknown patterns correctly', () => {
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expect(parseModelId('model-name-1')).toStrictEqual({
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activatedParams: null,
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modelName: 'model-name-1',
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orgName: null,
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params: null,
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quantization: null,
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raw: 'model-name-1',
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tags: []
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});
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expect(parseModelId('org/model-name-2')).toStrictEqual({
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activatedParams: null,
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modelName: 'model-name-2',
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orgName: 'org',
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params: null,
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quantization: null,
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raw: 'org/model-name-2',
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tags: []
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});
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});
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it('extracts model parameters correctly', () => {
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expect(parseModelId('model-100B-BF16')).toMatchObject({ params: '100B' });
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expect(parseModelId('model-100B:Q4_K_M')).toMatchObject({ params: '100B' });
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});
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it('extracts model parameters correctly in lowercase', () => {
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expect(parseModelId('model-100b-bf16')).toMatchObject({ params: '100B' });
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expect(parseModelId('model-100b:q4_k_m')).toMatchObject({ params: '100B' });
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});
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it('extracts activated parameters correctly', () => {
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expect(parseModelId('model-100B-A10B-BF16')).toMatchObject({ activatedParams: 'A10B' });
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expect(parseModelId('model-100B-A10B:Q4_K_M')).toMatchObject({ activatedParams: 'A10B' });
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});
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it('extracts activated parameters correctly in lowercase', () => {
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expect(parseModelId('model-100b-a10b-bf16')).toMatchObject({ activatedParams: 'A10B' });
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expect(parseModelId('model-100b-a10b:q4_k_m')).toMatchObject({ activatedParams: 'A10B' });
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});
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it('extracts quantization correctly', () => {
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// Dash-separated quantization
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expect(parseModelId('model-100B-UD-IQ1_S')).toMatchObject({ quantization: 'UD-IQ1_S' });
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expect(parseModelId('model-100B-IQ4_XS')).toMatchObject({ quantization: 'IQ4_XS' });
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expect(parseModelId('model-100B-Q4_K_M')).toMatchObject({ quantization: 'Q4_K_M' });
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expect(parseModelId('model-100B-Q8_0')).toMatchObject({ quantization: 'Q8_0' });
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expect(parseModelId('model-100B-UD-Q8_K_XL')).toMatchObject({ quantization: 'UD-Q8_K_XL' });
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expect(parseModelId('model-100B-F16')).toMatchObject({ quantization: 'F16' });
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expect(parseModelId('model-100B-BF16')).toMatchObject({ quantization: 'BF16' });
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expect(parseModelId('model-100B-MXFP4')).toMatchObject({ quantization: 'MXFP4' });
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// Colon-separated quantization
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expect(parseModelId('model-100B:UD-IQ1_S')).toMatchObject({ quantization: 'UD-IQ1_S' });
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expect(parseModelId('model-100B:IQ4_XS')).toMatchObject({ quantization: 'IQ4_XS' });
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expect(parseModelId('model-100B:Q4_K_M')).toMatchObject({ quantization: 'Q4_K_M' });
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expect(parseModelId('model-100B:Q8_0')).toMatchObject({ quantization: 'Q8_0' });
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expect(parseModelId('model-100B:UD-Q8_K_XL')).toMatchObject({ quantization: 'UD-Q8_K_XL' });
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expect(parseModelId('model-100B:F16')).toMatchObject({ quantization: 'F16' });
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expect(parseModelId('model-100B:BF16')).toMatchObject({ quantization: 'BF16' });
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expect(parseModelId('model-100B:MXFP4')).toMatchObject({ quantization: 'MXFP4' });
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// Dot-separated quantization
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expect(parseModelId('nomic-embed-text-v2-moe.Q4_K_M')).toMatchObject({
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quantization: 'Q4_K_M'
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});
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});
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it('extracts additional tags correctly', () => {
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expect(parseModelId('model-100B-foobar-Q4_K_M')).toMatchObject({ tags: ['foobar'] });
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expect(parseModelId('model-100B-A10B-foobar-1M-BF16')).toMatchObject({
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tags: ['foobar', '1M']
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});
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expect(parseModelId('model-100B-1M-foobar:UD-Q8_K_XL')).toMatchObject({
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tags: ['1M', 'foobar']
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});
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});
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it('filters out container format segments from tags', () => {
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expect(parseModelId('model-100B-GGUF-Instruct-BF16')).toMatchObject({
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tags: ['Instruct']
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});
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expect(parseModelId('model-100B-GGML-Instruct:Q4_K_M')).toMatchObject({
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tags: ['Instruct']
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});
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});
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it('handles real-world examples correctly', () => {
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expect(parseModelId('meta-llama/Llama-3.1-8B')).toStrictEqual({
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activatedParams: null,
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modelName: 'Llama-3.1',
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orgName: 'meta-llama',
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params: '8B',
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quantization: null,
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raw: 'meta-llama/Llama-3.1-8B',
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tags: []
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});
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expect(parseModelId('openai/gpt-oss-120b-MXFP4')).toStrictEqual({
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activatedParams: null,
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modelName: 'gpt-oss',
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orgName: 'openai',
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params: '120B',
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quantization: 'MXFP4',
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raw: 'openai/gpt-oss-120b-MXFP4',
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tags: []
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});
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expect(parseModelId('openai/gpt-oss-20b:Q4_K_M')).toStrictEqual({
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activatedParams: null,
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modelName: 'gpt-oss',
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orgName: 'openai',
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params: '20B',
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quantization: 'Q4_K_M',
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raw: 'openai/gpt-oss-20b:Q4_K_M',
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tags: []
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});
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expect(parseModelId('Qwen/Qwen3-Coder-30B-A3B-Instruct-1M-BF16')).toStrictEqual({
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activatedParams: 'A3B',
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modelName: 'Qwen3-Coder',
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orgName: 'Qwen',
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params: '30B',
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quantization: 'BF16',
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raw: 'Qwen/Qwen3-Coder-30B-A3B-Instruct-1M-BF16',
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tags: ['Instruct', '1M']
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});
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});
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it('handles real-world examples with quantization in segments', () => {
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expect(parseModelId('meta-llama/Llama-4-Scout-17B-16E-Instruct-Q4_K_M')).toStrictEqual({
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activatedParams: null,
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modelName: 'Llama-4-Scout',
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orgName: 'meta-llama',
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params: '17B',
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quantization: 'Q4_K_M',
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raw: 'meta-llama/Llama-4-Scout-17B-16E-Instruct-Q4_K_M',
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tags: ['16E', 'Instruct']
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});
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expect(parseModelId('MiniMaxAI/MiniMax-M2-IQ4_XS')).toStrictEqual({
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activatedParams: null,
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modelName: 'MiniMax-M2',
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orgName: 'MiniMaxAI',
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params: null,
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quantization: 'IQ4_XS',
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raw: 'MiniMaxAI/MiniMax-M2-IQ4_XS',
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tags: []
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});
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expect(parseModelId('MiniMaxAI/MiniMax-M2-UD-Q3_K_XL')).toStrictEqual({
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activatedParams: null,
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modelName: 'MiniMax-M2',
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orgName: 'MiniMaxAI',
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params: null,
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quantization: 'UD-Q3_K_XL',
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raw: 'MiniMaxAI/MiniMax-M2-UD-Q3_K_XL',
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tags: []
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});
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expect(parseModelId('mistralai/Devstral-2-123B-Instruct-2512-Q4_K_M')).toStrictEqual({
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activatedParams: null,
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modelName: 'Devstral-2',
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orgName: 'mistralai',
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params: '123B',
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quantization: 'Q4_K_M',
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raw: 'mistralai/Devstral-2-123B-Instruct-2512-Q4_K_M',
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tags: ['Instruct', '2512']
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});
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expect(parseModelId('mistralai/Devstral-Small-2-24B-Instruct-2512-Q8_0')).toStrictEqual({
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activatedParams: null,
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modelName: 'Devstral-Small-2',
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orgName: 'mistralai',
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params: '24B',
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quantization: 'Q8_0',
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raw: 'mistralai/Devstral-Small-2-24B-Instruct-2512-Q8_0',
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tags: ['Instruct', '2512']
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});
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expect(parseModelId('noctrex/GLM-4.7-Flash-MXFP4_MOE')).toStrictEqual({
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activatedParams: null,
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modelName: 'GLM-4.7-Flash',
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orgName: 'noctrex',
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params: null,
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quantization: 'MXFP4_MOE',
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raw: 'noctrex/GLM-4.7-Flash-MXFP4_MOE',
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tags: []
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});
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expect(parseModelId('Qwen/Qwen3-Coder-Next-Q4_K_M')).toStrictEqual({
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activatedParams: null,
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modelName: 'Qwen3-Coder-Next',
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orgName: 'Qwen',
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params: null,
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quantization: 'Q4_K_M',
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raw: 'Qwen/Qwen3-Coder-Next-Q4_K_M',
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tags: []
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});
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expect(parseModelId('openai/gpt-oss-120b-Q4_K_M')).toStrictEqual({
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activatedParams: null,
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modelName: 'gpt-oss',
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orgName: 'openai',
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params: '120B',
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quantization: 'Q4_K_M',
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raw: 'openai/gpt-oss-120b-Q4_K_M',
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tags: []
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});
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expect(parseModelId('openai/gpt-oss-20b-F16')).toStrictEqual({
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activatedParams: null,
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modelName: 'gpt-oss',
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orgName: 'openai',
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params: '20B',
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quantization: 'F16',
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raw: 'openai/gpt-oss-20b-F16',
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tags: []
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});
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expect(parseModelId('nomic-embed-text-v2-moe.Q4_K_M')).toStrictEqual({
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activatedParams: null,
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modelName: 'nomic-embed-text-v2-moe',
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orgName: null,
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params: null,
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quantization: 'Q4_K_M',
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raw: 'nomic-embed-text-v2-moe.Q4_K_M',
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tags: []
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});
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});
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it('handles ambiguous model names', () => {
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// Qwen3.5 Instruct vs Thinking — tags should distinguish them
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expect(parseModelId('Qwen/Qwen3.5-30B-A3B-Instruct')).toMatchObject({
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modelName: 'Qwen3.5',
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params: '30B',
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activatedParams: 'A3B',
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tags: ['Instruct']
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});
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expect(parseModelId('Qwen/Qwen3.5-30B-A3B-Thinking')).toMatchObject({
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modelName: 'Qwen3.5',
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params: '30B',
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activatedParams: 'A3B',
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tags: ['Thinking']
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});
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// Dot-separated quantization with variant suffixes
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expect(parseModelId('gemma-3-27b-it-heretic-v2.Q8_0')).toMatchObject({
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modelName: 'gemma-3',
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params: '27B',
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quantization: 'Q8_0',
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tags: ['it', 'heretic', 'v2']
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});
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expect(parseModelId('gemma-3-27b-it.Q8_0')).toMatchObject({
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modelName: 'gemma-3',
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params: '27B',
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quantization: 'Q8_0',
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tags: ['it']
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});
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});
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});
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