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TGlideTGlide
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fetch hf models and providers
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6 files changed

+371
-8
lines changed

6 files changed

+371
-8
lines changed

‎src/api/huggingface-models.ts‎

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import { fetchHuggingFaceModels, type HuggingFaceModel } from "../services/huggingface-models"
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export interface HuggingFaceModelsResponse {
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models: HuggingFaceModel[]
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cached: boolean
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timestamp: number
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}
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export async function getHuggingFaceModels(): Promise<HuggingFaceModelsResponse> {
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const models = await fetchHuggingFaceModels()
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return {
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models,
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cached: false, // We could enhance this to track if data came from cache
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timestamp: Date.now(),
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}
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}

‎src/core/webview/webviewMessageHandler.ts‎

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@@ -674,6 +674,22 @@ export const webviewMessageHandler = async (
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// TODO: Cache like we do for OpenRouter, etc?
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provider.postMessageToWebview({ type: "vsCodeLmModels", vsCodeLmModels })
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break
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case "requestHuggingFaceModels":
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try {
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const { getHuggingFaceModels } = await import("../../api/huggingface-models")
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const huggingFaceModelsResponse = await getHuggingFaceModels()
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provider.postMessageToWebview({
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type: "huggingFaceModels",
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huggingFaceModels: huggingFaceModelsResponse.models,
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})
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} catch (error) {
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console.error("Failed to fetch Hugging Face models:", error)
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provider.postMessageToWebview({
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type: "huggingFaceModels",
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huggingFaceModels: [],
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})
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}
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break
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case "openImage":
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openImage(message.text!, { values: message.values })
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break

‎src/services/huggingface-models.ts‎

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export interface HuggingFaceModel {
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_id: string
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id: string
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inferenceProviderMapping: InferenceProviderMapping[]
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trendingScore: number
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config: ModelConfig
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tags: string[]
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pipeline_tag: "text-generation" | "image-text-to-text"
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library_name?: string
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}
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export interface InferenceProviderMapping {
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provider: string
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providerId: string
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status: "live" | "staging" | "error"
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task: "conversational"
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}
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export interface ModelConfig {
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architectures: string[]
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model_type: string
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tokenizer_config?: {
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chat_template?: string | Array<{ name: string; template: string }>
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model_max_length?: number
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}
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}
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interface HuggingFaceApiParams {
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pipeline_tag?: "text-generation" | "image-text-to-text"
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filter: string
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inference_provider: string
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limit: number
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expand: string[]
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}
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const DEFAULT_PARAMS: HuggingFaceApiParams = {
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filter: "conversational",
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inference_provider: "all",
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limit: 100,
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expand: [
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"inferenceProviderMapping",
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"config",
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"library_name",
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"pipeline_tag",
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"tags",
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"mask_token",
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"trendingScore",
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],
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}
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const BASE_URL = "https://huggingface.co/api/models"
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const CACHE_DURATION = 1000 * 60 * 60 // 1 hour
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interface CacheEntry {
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data: HuggingFaceModel[]
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timestamp: number
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status: "success" | "partial" | "error"
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}
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let cache: CacheEntry | null = null
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function buildApiUrl(params: HuggingFaceApiParams): string {
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const url = new URL(BASE_URL)
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// Add simple params
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Object.entries(params).forEach(([key, value]) => {
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if (!Array.isArray(value)) {
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url.searchParams.append(key, String(value))
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}
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})
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// Handle array params specially
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params.expand.forEach((item) => {
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url.searchParams.append("expand[]", item)
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})
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return url.toString()
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}
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const headers: HeadersInit = {
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"Upgrade-Insecure-Requests": "1",
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"Sec-Fetch-Dest": "document",
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"Sec-Fetch-Mode": "navigate",
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"Sec-Fetch-Site": "none",
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"Sec-Fetch-User": "?1",
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Priority: "u=0, i",
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Pragma: "no-cache",
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"Cache-Control": "no-cache",
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}
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const requestInit: RequestInit = {
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credentials: "include",
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headers,
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method: "GET",
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mode: "cors",
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}
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export async function fetchHuggingFaceModels(): Promise<HuggingFaceModel[]> {
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const now = Date.now()
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// Check cache
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if (cache && now - cache.timestamp < CACHE_DURATION) {
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console.log("Using cached Hugging Face models")
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return cache.data
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}
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try {
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console.log("Fetching Hugging Face models from API...")
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// Fetch both text-generation and image-text-to-text models in parallel
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const [textGenResponse, imgTextResponse] = await Promise.allSettled([
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fetch(buildApiUrl({ ...DEFAULT_PARAMS, pipeline_tag: "text-generation" }), requestInit),
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fetch(buildApiUrl({ ...DEFAULT_PARAMS, pipeline_tag: "image-text-to-text" }), requestInit),
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])
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let textGenModels: HuggingFaceModel[] = []
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let imgTextModels: HuggingFaceModel[] = []
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let hasErrors = false
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// Process text-generation models
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if (textGenResponse.status === "fulfilled" && textGenResponse.value.ok) {
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textGenModels = await textGenResponse.value.json()
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} else {
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console.error("Failed to fetch text-generation models:", textGenResponse)
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hasErrors = true
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}
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// Process image-text-to-text models
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if (imgTextResponse.status === "fulfilled" && imgTextResponse.value.ok) {
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imgTextModels = await imgTextResponse.value.json()
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} else {
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console.error("Failed to fetch image-text-to-text models:", imgTextResponse)
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hasErrors = true
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}
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// Combine and filter models
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const allModels = [...textGenModels, ...imgTextModels]
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.filter((model) => model.inferenceProviderMapping.length > 0)
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.sort((a, b) => a.id.toLowerCase().localeCompare(b.id.toLowerCase()))
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// Update cache
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cache = {
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data: allModels,
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timestamp: now,
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status: hasErrors ? "partial" : "success",
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}
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console.log(`Fetched ${allModels.length} Hugging Face models (status: ${cache.status})`)
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return allModels
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} catch (error) {
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console.error("Error fetching Hugging Face models:", error)
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// Return cached data if available
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if (cache) {
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console.log("Using stale cached data due to fetch error")
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cache.status = "error"
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return cache.data
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}
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// No cache available, return empty array
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return []
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}
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}
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export function getCachedModels(): HuggingFaceModel[] | null {
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return cache?.data || null
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}
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export function clearCache(): void {
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cache = null
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}

‎src/shared/ExtensionMessage.ts‎

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@@ -67,6 +67,7 @@ export interface ExtensionMessage {
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| "ollamaModels"
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| "lmStudioModels"
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| "vsCodeLmModels"
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| "huggingFaceModels"
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| "vsCodeLmApiAvailable"
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| "updatePrompt"
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| "systemPrompt"
@@ -135,6 +136,28 @@ export interface ExtensionMessage {
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ollamaModels?: string[]
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lmStudioModels?: string[]
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vsCodeLmModels?: { vendor?: string; family?: string; version?: string; id?: string }[]
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huggingFaceModels?: Array<{
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_id: string
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id: string
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inferenceProviderMapping: Array<{
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provider: string
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providerId: string
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status: "live" | "staging" | "error"
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task: "conversational"
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}>
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trendingScore: number
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config: {
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architectures: string[]
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model_type: string
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tokenizer_config?: {
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chat_template?: string | Array<{ name: string; template: string }>
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model_max_length?: number
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}
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}
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tags: string[]
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pipeline_tag: "text-generation" | "image-text-to-text"
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library_name?: string
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}>
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mcpServers?: McpServer[]
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commits?: GitCommit[]
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listApiConfig?: ProviderSettingsEntry[]

‎src/shared/WebviewMessage.ts‎

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@@ -67,6 +67,7 @@ export interface WebviewMessage {
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| "requestOllamaModels"
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| "requestLmStudioModels"
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| "requestVsCodeLmModels"
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| "requestHuggingFaceModels"
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| "openImage"
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| "saveImage"
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| "openFile"

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