more models, nodemon fix, thinking fixes, settings ui fixes, clean
This commit is contained in:
parent
4409acd79d
commit
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7 changed files with 211 additions and 106 deletions
94
config/ai.ts
94
config/ai.ts
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@ -2,7 +2,7 @@ import type { ModelInfo } from "../src/commands/ai"
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export const defaultFlashModel = "gemma3:4b"
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export const defaultThinkingModel = "qwen3:4b"
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export const unloadModelAfterB = 0.1 // how many billion params until model is auto-unloaded
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export const unloadModelAfterB = 12 // how many billion params until model is auto-unloaded
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export const models: ModelInfo[] = [
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{
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@ -35,13 +35,31 @@ export const models: ModelInfo[] = [
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descriptionPt: 'Qwen3 é uma série de modelos multilingues.',
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models: [
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{ name: 'qwen3:0.6b', label: 'Qwen3 0.6B', parameterSize: '0.6B' },
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{ name: 'qwen3:1.7b', label: 'Qwen3 1.7B', parameterSize: '1.7B' },
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{ name: 'qwen3:4b', label: 'Qwen3 4B', parameterSize: '4B' },
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{ name: 'qwen3:8b', label: 'Qwen3 8B', parameterSize: '8B' },
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{ name: 'qwen3:14b', label: 'Qwen3 14B', parameterSize: '14B' },
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{ name: 'qwen3:30b', label: 'Qwen3 30B', parameterSize: '30B' },
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{ name: 'qwen3:32b', label: 'Qwen3 32B', parameterSize: '32B' },
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{ name: 'qwen3:235b-a22b', label: 'Qwen3 235B A22B', parameterSize: '235B' },
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]
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},
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{
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name: 'qwen3-abliterated',
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label: 'Qwen3 [ Uncensored ]',
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descriptionEn: 'Qwen3-abliterated is a multilingual reasoning model series.',
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descriptionPt: 'Qwen3-abliterated é uma série de modelos multilingues.',
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models: [
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{ name: 'huihui_ai/qwen3-abliterated:0.6b', label: 'Qwen3 Uncensored 0.6B', parameterSize: '0.6B' },
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{ name: 'huihui_ai/qwen3-abliterated:1.7b', label: 'Qwen3 Uncensored 1.7B', parameterSize: '1.7B' },
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{ name: 'huihui_ai/qwen3-abliterated:4b', label: 'Qwen3 Uncensored 4B', parameterSize: '4B' },
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{ name: 'huihui_ai/qwen3-abliterated:8b', label: 'Qwen3 Uncensored 8B', parameterSize: '8B' },
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{ name: 'huihui_ai/qwen3-abliterated:14b', label: 'Qwen3 Uncensored 14B', parameterSize: '14B' },
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{ name: 'huihui_ai/qwen3-abliterated:30b', label: 'Qwen3 Uncensored 30B', parameterSize: '30B' },
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{ name: 'huihui_ai/qwen3-abliterated:32b', label: 'Qwen3 Uncensored 32B', parameterSize: '32B' },
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{ name: 'huihui_ai/qwen3-abliterated:235b', label: 'Qwen3 Uncensored 235B', parameterSize: '235B' },
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]
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},
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{
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name: 'qwq',
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label: 'QwQ',
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@ -49,6 +67,7 @@ export const models: ModelInfo[] = [
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descriptionPt: 'QwQ é o modelo de raciocínio da série Qwen.',
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models: [
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{ name: 'qwq:32b', label: 'QwQ 32B', parameterSize: '32B' },
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{ name: 'huihui_ai/qwq-abliterated:32b', label: 'QwQ Uncensored 32B', parameterSize: '32B' },
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]
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},
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{
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@ -60,6 +79,32 @@ export const models: ModelInfo[] = [
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{ name: 'llama4:scout', label: 'Llama4 109B A17B', parameterSize: '109B' },
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]
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},
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{
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name: 'deepseek',
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label: 'DeepSeek [ & Uncensored ]',
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descriptionEn: 'DeepSeek is a research model for reasoning tasks.',
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descriptionPt: 'DeepSeek é um modelo de pesquisa para tarefas de raciocínio.',
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models: [
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{ name: 'deepseek-r1:1.5b', label: 'DeepSeek 1.5B', parameterSize: '1.5B' },
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{ name: 'deepseek-r1:7b', label: 'DeepSeek 7B', parameterSize: '7B' },
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{ name: 'deepseek-r1:8b', label: 'DeepSeek 8B', parameterSize: '8B' },
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{ name: 'deepseek-r1:14b', label: 'DeepSeek 14B', parameterSize: '14B' },
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{ name: 'huihui_ai/deepseek-r1-abliterated:1.5b', label: 'DeepSeek Uncensored 1.5B', parameterSize: '1.5B' },
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{ name: 'huihui_ai/deepseek-r1-abliterated:7b', label: 'DeepSeek Uncensored 7B', parameterSize: '7B' },
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{ name: 'huihui_ai/deepseek-r1-abliterated:8b', label: 'DeepSeek Uncensored 8B', parameterSize: '8B' },
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{ name: 'huihui_ai/deepseek-r1-abliterated:14b', label: 'DeepSeek Uncensored 14B', parameterSize: '14B' },
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]
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},
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{
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name: 'hermes3',
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label: 'Hermes3',
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descriptionEn: 'Hermes 3 is the latest version of the flagship Hermes series of LLMs by Nous Research.',
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descriptionPt: 'Hermes 3 é a versão mais recente da série Hermes de LLMs da Nous Research.',
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models: [
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{ name: 'hermes3:3b', label: 'Hermes3 3B', parameterSize: '3B' },
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{ name: 'hermes3:8b', label: 'Hermes3 8B', parameterSize: '8B' },
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]
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},
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{
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name: 'mistral',
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label: 'Mistral',
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@ -70,15 +115,15 @@ export const models: ModelInfo[] = [
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]
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},
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{
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name: 'deepseek',
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label: 'DeepSeek [ & Uncensored ]',
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descriptionEn: 'DeepSeek is a research model for reasoning tasks.',
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descriptionPt: 'DeepSeek é um modelo de pesquisa para tarefas de raciocínio.',
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name: 'phi4 [ & Uncensored ]',
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label: 'Phi4',
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descriptionEn: 'Phi-4 is a 14B parameter, state-of-the-art open model from Microsoft. ',
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descriptionPt: 'Phi-4 é um modelo de 14B de última geração, aberto pela Microsoft.',
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models: [
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{ name: 'deepseek-r1:1.5b', label: 'DeepSeek 1.5B', parameterSize: '1.5B' },
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{ name: 'deepseek-r1:7b', label: 'DeepSeek 7B', parameterSize: '7B' },
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{ name: 'huihui_ai/deepseek-r1-abliterated:1.5b', label: 'DeepSeek Uncensored 1.5B', parameterSize: '1.5B' },
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{ name: 'huihui_ai/deepseek-r1-abliterated:7b', label: 'DeepSeek Uncensored 7B', parameterSize: '7B' },
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{ name: 'hf.co/unsloth/Phi-4-mini-reasoning-GGUF', label: 'Phi4 Mini Reasoning', parameterSize: '4B' },
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{ name: 'phi4:14b', label: 'Phi4 14B', parameterSize: '14B' },
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{ name: 'hf.co/unsloth/Phi-4-reasoning-plus-GGUF', label: 'Phi4 Reasoning Plus', parameterSize: '14B' },
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{ name: 'huihui_ai/phi4-abliterated:14b', label: 'Phi4 Uncensored 14B', parameterSize: '14B' },
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]
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},
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{
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@ -89,5 +134,34 @@ export const models: ModelInfo[] = [
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models: [
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{ name: 'phi3:3.8b', label: 'Phi3 3.8B', parameterSize: '3.8B' },
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]
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}
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},
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{
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name: 'llama3',
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label: 'Llama4',
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descriptionEn: 'Llama 3, a lightweight model from Meta.',
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descriptionPt: 'Llama 3, um modelo leve da Meta.',
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models: [
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{ name: 'llama3:8b', label: 'Llama3 8B', parameterSize: '8B' },
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]
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},
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{
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name: 'llama3.1 [ Uncensored ]',
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label: 'Llama3.1',
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descriptionEn: 'Ablitered v3 llama-3.1 8b with uncensored prompt ',
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descriptionPt: 'Llama3.1 é um modelo aberto, leve e para dispositivos locais, com prompt não censurado.',
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models: [
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{ name: 'mannix/llama3.1-8b-abliterated:latest', label: 'Llama3.1 8B', parameterSize: '8B' },
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]
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},
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{
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name: 'llama3.2 [ & Uncensored ]',
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label: 'Llama3.2',
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descriptionEn: 'Llama3.2 is a family of open, lightweight models for general tasks.',
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descriptionPt: 'Llama3.2 é uma família de modelos abertos, leves e para dispositivos locais, para tarefas gerais.',
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models: [
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{ name: 'llama3.2:1b', label: 'Llama3.2 1B', parameterSize: '1B' },
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{ name: 'llama3.2:3b', label: 'Llama3.2 3B', parameterSize: '3B' },
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{ name: 'socialnetwooky/llama3.2-abliterated:3b_q8_0', label: 'Llama3.2 Uncensored 3B', parameterSize: '3B' },
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]
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},
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];
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2
config/settings.ts
Normal file
2
config/settings.ts
Normal file
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@ -0,0 +1,2 @@
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export const seriesPageSize = 4;
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export const modelPageSize = 4;
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@ -1,6 +1,6 @@
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{
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"ignore": ["src/props/*.json", "src/props/*.txt"],
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"watch": ["src"],
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"watch": ["src", "config"],
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"ext": "ts,js",
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"exec": "bun src/bot.ts"
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}
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@ -135,6 +135,26 @@ function sanitizeMarkdownForTelegram(text: string): string {
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return sanitizedText;
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}
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function processThinkingTags(text: string): string {
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let processedText = text;
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const firstThinkIndex = processedText.indexOf('<think>');
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if (firstThinkIndex === -1) {
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return processedText.replace(/<\/think>/g, '___THINK_END___');
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}
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processedText = processedText.substring(0, firstThinkIndex) + '___THINK_START___' + processedText.substring(firstThinkIndex + '<think>'.length);
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const lastThinkEndIndex = processedText.lastIndexOf('</think>');
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if (lastThinkEndIndex !== -1) {
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processedText = processedText.substring(0, lastThinkEndIndex) + '___THEND___' + processedText.substring(lastThinkEndIndex + '</think>'.length);
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}
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processedText = processedText.replace(/<think>/g, '');
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processedText = processedText.replace(/<\/think>/g, '');
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processedText = processedText.replace('___THEND___', '___THINK_END___');
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return processedText;
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}
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export async function preChecks() {
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const envs = [
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"ollamaApi",
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@ -221,9 +241,10 @@ async function getResponse(prompt: string, ctx: TextContext, replyGenerating: Me
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error: Strings.unexpectedErr.replace("{error}", Strings.ai.noChatFound),
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};
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}
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const cleanedModelName = model.replace('hf.co/', '');
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let status = Strings.ai.statusWaitingRender;
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let modelHeader = Strings.ai.modelHeader
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.replace("{model}", model)
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.replace("{model}", cleanedModelName)
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.replace("{temperature}", aiTemperature)
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.replace("{status}", status) + "\n\n";
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@ -277,23 +298,8 @@ async function getResponse(prompt: string, ctx: TextContext, replyGenerating: Me
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console.error("[✨ AI | !] Error parsing chunk");
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continue;
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}
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if (model === thinking_model && !showThinking) {
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if (ln.response) {
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finalResponseText += ln.response;
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if (finalResponseText.includes('<think>') && !thinkingMessageSent) {
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await rateLimiter.editMessageWithRetry(
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ctx,
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ctx.chat.id,
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replyGenerating.message_id,
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modelHeader + Strings.ai.thinking,
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{ parse_mode: 'Markdown' }
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);
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thinkingMessageSent = true;
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}
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}
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continue;
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}
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if (model === thinking_model && ln.response) {
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if (ln.response.includes('<think>')) {
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const thinkMatch = ln.response.match(/<think>([\s\S]*?)<\/think>/);
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if (thinkMatch && thinkMatch[1].trim().length > 0) {
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@ -304,68 +310,63 @@ async function getResponse(prompt: string, ctx: TextContext, replyGenerating: Me
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} else if (ln.response.includes('</think>')) {
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logger.logThinking(ctx.chat.id, replyGenerating.message_id, false);
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}
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}
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if (ln.response) {
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if (model === thinking_model) {
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let patchedThoughts = ln.response;
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const thinkTagRx = /<think>([\s\S]*?)<\/think>/g;
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patchedThoughts = patchedThoughts.replace(thinkTagRx, (p1) => p1.trim().length > 0 ? '___THINK_START___' + p1.trim() + '___THINK_END___' : '');
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patchedThoughts = patchedThoughts.replace(/<think>/g, '___THINK_START___');
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patchedThoughts = patchedThoughts.replace(/<\/think>/g, '___THINK_END___');
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thoughts += patchedThoughts;
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fullResponse += patchedThoughts;
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} else {
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fullResponse += ln.response;
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}
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if (showThinking) {
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let displayResponse = processThinkingTags(fullResponse);
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if (firstChunk) {
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status = Strings.ai.statusWaitingRender;
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modelHeader = Strings.ai.modelHeader
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.replace("{model}", model)
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.replace("{model}", cleanedModelName)
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.replace("{temperature}", aiTemperature)
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.replace("{status}", status) + "\n\n";
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await rateLimiter.editMessageWithRetry(
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ctx,
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ctx.chat.id,
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replyGenerating.message_id,
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modelHeader + formatThinkingMessage(fullResponse),
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modelHeader + formatThinkingMessage(displayResponse),
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{ parse_mode: 'Markdown' }
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);
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lastUpdateCharCount = fullResponse.length;
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lastUpdateCharCount = displayResponse.length;
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sentHeader = true;
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firstChunk = false;
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continue;
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}
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const updateEveryChars = Number(process.env.updateEveryChars) || 100;
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if (fullResponse.length - lastUpdateCharCount >= updateEveryChars || !sentHeader) {
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if (displayResponse.length - lastUpdateCharCount >= updateEveryChars || !sentHeader) {
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await rateLimiter.editMessageWithRetry(
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ctx,
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ctx.chat.id,
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replyGenerating.message_id,
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modelHeader + formatThinkingMessage(fullResponse),
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modelHeader + formatThinkingMessage(displayResponse),
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{ parse_mode: 'Markdown' }
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);
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lastUpdateCharCount = fullResponse.length;
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lastUpdateCharCount = displayResponse.length;
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sentHeader = true;
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}
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}
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}
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}
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if (model === thinking_model && !showThinking) {
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const cleanedResponse = finalResponseText.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
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return { success: true, response: cleanedResponse };
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}
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status = Strings.ai.statusRendering;
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modelHeader = Strings.ai.modelHeader
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.replace("{model}", model)
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.replace("{model}", cleanedModelName)
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.replace("{temperature}", aiTemperature)
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.replace("{status}", status) + "\n\n";
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if (showThinking) {
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let displayResponse = processThinkingTags(fullResponse);
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await rateLimiter.editMessageWithRetry(
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ctx,
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ctx.chat.id,
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replyGenerating.message_id,
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modelHeader + formatThinkingMessage(fullResponse),
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modelHeader + formatThinkingMessage(displayResponse),
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{ parse_mode: 'Markdown' }
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);
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}
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const responseCharCount = fullResponse.length;
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await db.update(schema.usersTable)
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.set({
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@ -373,9 +374,12 @@ async function getResponse(prompt: string, ctx: TextContext, replyGenerating: Me
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aiRequests: sql`${schema.usersTable.aiRequests} + 1`
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})
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.where(eq(schema.usersTable.telegramId, userId));
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const patchedResponse = processThinkingTags(fullResponse);
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return {
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success: true,
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response: fullResponse,
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response: patchedResponse,
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};
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} catch (error: unknown) {
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const errorMsg = extractAxiosErrorMessage(error);
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@ -429,16 +433,20 @@ async function handleAiReply(ctx: TextContext, model: string, prompt: string, re
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if (!aiResponse) return;
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if (!ctx.chat) return;
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if (aiResponse.success && aiResponse.response) {
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const cleanedModelName = model.replace('hf.co/', '');
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const status = Strings.ai.statusComplete;
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const modelHeader = Strings.ai.modelHeader
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.replace("{model}", model)
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.replace("{model}", cleanedModelName)
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.replace("{temperature}", aiTemperature)
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.replace("{status}", status) + "\n\n";
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const urlWarning = containsUrls(originalMessage) ? Strings.ai.urlWarning : '';
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let finalResponse = aiResponse.response;
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if (model === thinking_model) {
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if (showThinking) {
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finalResponse = finalResponse.replace(/___THINK_START___/g, `${Strings.ai.thinking}`)
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.replace(/___THINK_END___/g, `${Strings.ai.finishedThinking}`);
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} else {
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finalResponse = finalResponse.replace(/___THINK_START___[\s\S]*?___THINK_END___/g, '').trim();
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finalResponse = finalResponse.replace(/___THINK_START___[\s\S]*/g, '').trim();
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}
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await rateLimiter.editMessageWithRetry(
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@ -10,6 +10,7 @@ import type { NodePgDatabase } from 'drizzle-orm/node-postgres';
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import { getModelLabelByName } from './ai';
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import { models } from '../../config/ai';
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import { langs } from '../locales/config';
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import { modelPageSize, seriesPageSize } from '../../config/settings';
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type UserRow = typeof schema.usersTable.$inferSelect;
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@ -221,12 +222,12 @@ export default (bot: Telegraf<Context>, db: NodePgDatabase<typeof schema>) => {
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const originalIndex = start + idx;
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const isSelected = series.models.some(m => m.name === user.customAiModel);
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const label = isSelected ? `✅ ${series.label}` : series.label;
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return { text: label, callback_data: `selectseries_${originalIndex}_${user.telegramId}` };
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return { text: label, callback_data: `selectseries_${originalIndex}_0_${user.telegramId}` };
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});
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const navigationButtons: any[] = [];
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if (page > 0) {
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navigationButtons.push({ text: Strings.varStrings.varBack, callback_data: `settings_aiModel_${page - 1}_${user.telegramId}` });
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navigationButtons.push({ text: Strings.varStrings.varLess, callback_data: `settings_aiModel_${page - 1}_${user.telegramId}` });
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}
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if (end < models.length) {
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navigationButtons.push({ text: Strings.varStrings.varMore, callback_data: `settings_aiModel_${page + 1}_${user.telegramId}` });
|
||||
|
|
@ -257,7 +258,7 @@ export default (bot: Telegraf<Context>, db: NodePgDatabase<typeof schema>) => {
|
|||
}
|
||||
});
|
||||
|
||||
bot.action(/^selectseries_\d+_\d+$/, async (ctx) => {
|
||||
bot.action(/^selectseries_\d+_\d+_\d+$/, async (ctx) => {
|
||||
const data = (ctx.callbackQuery as any).data;
|
||||
const userId = extractUserIdFromCallback(data);
|
||||
const allowed = !!userId && String(ctx.from.id) === userId;
|
||||
|
|
@ -269,26 +270,46 @@ export default (bot: Telegraf<Context>, db: NodePgDatabase<typeof schema>) => {
|
|||
await ctx.answerCbQuery();
|
||||
const { user, Strings } = await getUserAndStrings(ctx, db);
|
||||
if (!user) return;
|
||||
const match = data.match(/^selectseries_(\d+)_\d+$/);
|
||||
const match = data.match(/^selectseries_(\d+)_(\d+)_(\d+)$/);
|
||||
if (!match) return;
|
||||
const seriesIdx = parseInt(match[1], 10);
|
||||
const modelPage = parseInt(match[2], 10);
|
||||
const series = models[seriesIdx];
|
||||
if (!series) return;
|
||||
const pageSize = 4;
|
||||
const page = Math.floor(seriesIdx / pageSize);
|
||||
|
||||
const seriesPage = Math.floor(seriesIdx / seriesPageSize);
|
||||
|
||||
const start = modelPage * modelPageSize;
|
||||
const end = start + modelPageSize;
|
||||
const paginatedSeriesModels = series.models.slice(start, end);
|
||||
|
||||
const modelButtons = paginatedSeriesModels.map((m, idx) => {
|
||||
const originalModelIndex = start + idx;
|
||||
const isSelected = m.name === user.customAiModel;
|
||||
const label = isSelected ? `✅ ${m.label}` : m.label;
|
||||
return [{ text: `${label} (${m.parameterSize})`, callback_data: `setmodel_${seriesIdx}_${originalModelIndex}_${user.telegramId}` }];
|
||||
});
|
||||
|
||||
const navigationButtons: any[] = [];
|
||||
if (modelPage > 0) {
|
||||
navigationButtons.push({ text: Strings.varStrings.varLess, callback_data: `selectseries_${seriesIdx}_${modelPage - 1}_${user.telegramId}` });
|
||||
}
|
||||
if (end < series.models.length) {
|
||||
navigationButtons.push({ text: Strings.varStrings.varMore, callback_data: `selectseries_${seriesIdx}_${modelPage + 1}_${user.telegramId}` });
|
||||
}
|
||||
|
||||
const keyboard: any[][] = [...modelButtons];
|
||||
if (navigationButtons.length > 0) {
|
||||
keyboard.push(navigationButtons);
|
||||
}
|
||||
keyboard.push([{ text: `${Strings.varStrings.varBack}`, callback_data: `settings_aiModel_${seriesPage}_${user.telegramId}` }]);
|
||||
const desc = user.languageCode === 'pt' ? series.descriptionPt : series.descriptionEn;
|
||||
try {
|
||||
await ctx.editMessageText(
|
||||
`${Strings.settings.ai.seriesDescription.replace('{seriesDescription}', desc)}\n\n${Strings.settings.ai.selectParameterSize.replace('{seriesLabel}', series.label).replace(' [ & Uncensored ]', '')}\n\n${Strings.settings.ai.parameterSizeExplanation}`,
|
||||
{
|
||||
reply_markup: {
|
||||
inline_keyboard: series.models.map((m, idx) => {
|
||||
const isSelected = m.name === user.customAiModel;
|
||||
const label = isSelected ? `✅ ${m.label}` : m.label;
|
||||
return [{ text: `${label} (${m.parameterSize})`, callback_data: `setmodel_${seriesIdx}_${idx}_${user.telegramId}` }];
|
||||
}).concat([[
|
||||
{ text: `${Strings.varStrings.varBack}`, callback_data: `settings_aiModel_${page}_${user.telegramId}` }
|
||||
]])
|
||||
inline_keyboard: keyboard
|
||||
}
|
||||
}
|
||||
);
|
||||
|
|
|
|||
|
|
@ -80,7 +80,7 @@
|
|||
"inQueue": "ℹ️ You are {position} in the queue.",
|
||||
"startingProcessing": "✨ Starting to process your request...",
|
||||
"systemPrompt": "You are a friendly assistant called {botName}.\nCurrent Date/Time (UTC): {date}\n\n---\n\nUser message:\n{message}",
|
||||
"statusWaitingRender": "⏳ Waiting to Render...",
|
||||
"statusWaitingRender": "⏳ Streaming...",
|
||||
"statusRendering": "🖼️ Rendering...",
|
||||
"statusComplete": "✅ Complete!",
|
||||
"modelHeader": "🤖 *{model}* 🌡️ *{temperature}* {status}",
|
||||
|
|
@ -123,7 +123,7 @@
|
|||
"aiEnabledSetTo": "AI Enabled set to {aiEnabled}",
|
||||
"aiModelSetTo": "AI Model set to {aiModel}",
|
||||
"aiTemperatureSetTo": "AI Temperature set to {aiTemperature}",
|
||||
"selectSeries": "*Please select a model series.*",
|
||||
"selectSeries": "*Please select a model series.*\n\nThis will be set as the default model for the /ai command.",
|
||||
"seriesDescription": "{seriesDescription}",
|
||||
"selectParameterSize": "*Please select a parameter size for {seriesLabel}*.",
|
||||
"parameterSizeExplanation": "Parameter size (e.g. 2B, 4B) refers to the number of parameters in the model. Larger models may be more capable but require more resources.",
|
||||
|
|
|
|||
|
|
@ -85,7 +85,7 @@
|
|||
"selectParameterSize": "Por favor, selecione um tamanho de parâmetro para {seriesLabel}.",
|
||||
"parameterSizeExplanation": "O tamanho do parâmetro (ex: 2B, 4B) refere-se ao número de parâmetros do modelo. Modelos maiores podem ser mais capazes, mas exigem mais recursos.",
|
||||
"systemPrompt": "Você é um assistente de Telegram chamado {botName}.\nData/Hora atual (UTC): {date}\n\n---\n\nMensagem do usuário:\n{message}",
|
||||
"statusWaitingRender": "⏳ Aguardando renderização...",
|
||||
"statusWaitingRender": "⏳ Transmitindo...",
|
||||
"statusRendering": "🖼️ Renderizando...",
|
||||
"statusComplete": "✅ Completo!",
|
||||
"modelHeader": "🤖 *{model}* 🌡️ *{temperature}* {status}",
|
||||
|
|
@ -126,7 +126,7 @@
|
|||
"aiEnabledSetTo": "Inteligência Artificial definido para {aiEnabled}",
|
||||
"aiModelSetTo": "Modelo personalizado definido para {aiModel}",
|
||||
"aiTemperatureSetTo": "Temperatura definida para {aiTemperature}",
|
||||
"selectSeries": "*Por favor, selecione uma série de modelos.*",
|
||||
"selectSeries": "*Por favor, selecione uma série de modelos.*\n\nIsso será definido como o modelo padrão para o comando /ai.",
|
||||
"seriesDescription": "{seriesDescription}",
|
||||
"selectParameterSize": "Por favor, selecione um tamanho de parâmetro para {seriesLabel}.",
|
||||
"parameterSizeExplanation": "O tamanho do parâmetro (ex: 2B, 4B) refere-se ao número de parâmetros do modelo. Modelos maiores podem ser mais capazes, mas exigem mais recursos.",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue