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import { HF_ACCESS_TOKEN, MODELS } from "$env/static/private";
import { z } from "zod";
const modelsRaw = z
.array(
z.object({
/** Used as an identifier in DB */
id: z.string().optional(),
/** Used to link to the model page, and for inference */
name: z.string().min(1),
displayName: z.string().min(1).optional(),
description: z.string().min(1).optional(),
websiteUrl: z.string().url().optional(),
datasetName: z.string().min(1).optional(),
userMessageToken: z.string().min(1),
assistantMessageToken: z.string().min(1),
messageEndToken: z.string().min(1).optional(),
preprompt: z.string().default(""),
prepromptUrl: z.string().url().optional(),
promptExamples: z
.array(
z.object({
title: z.string().min(1),
prompt: z.string().min(1),
})
)
.optional(),
endpoints: z
.array(
z.object({
url: z.string().url(),
authorization: z.string().min(1).default(`Bearer ${HF_ACCESS_TOKEN}`),
weight: z.number().int().positive().default(1),
})
)
.optional(),
parameters: z
.object({
temperature: z.number().min(0).max(1),
truncate: z.number().int().positive(),
max_new_tokens: z.number().int().positive(),
stop: z.array(z.string()).optional(),
})
.passthrough(),
})
)
.parse(JSON.parse(MODELS));
export const models = await Promise.all(
modelsRaw.map(async (m) => ({
...m,
id: m.id || m.name,
displayName: m.displayName || m.name,
preprompt: m.prepromptUrl ? await fetch(m.prepromptUrl).then((r) => r.text()) : m.preprompt,
}))
);
export type BackendModel = (typeof models)[0];
export const defaultModel = models[0];
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