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.tokenized-text { width:100%; padding:2rem; max-height: 400px; overflow-y: auto; box-sizing:border-box; line-height:4rem; /* Lots of space between lines */ font-family: "Roboto Light", "Ubuntu Light", "Ubuntu", monospace; box-shadow: 2px 2px 2px rgba(0,0,0,0.2); background-color: rgb...
tokenizers/bindings/python/py_src/tokenizers/tools/visualizer-styles.css/0
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use std::sync::{Arc, RwLock}; use pyo3::exceptions; use pyo3::exceptions::PyException; use pyo3::prelude::*; use pyo3::types::*; use serde::ser::SerializeStruct; use serde::{Deserialize, Deserializer, Serialize, Serializer}; use tk::normalizer::SplitDelimiterBehavior; use tk::pre_tokenizers::bert::BertPreTokenizer; u...
tokenizers/bindings/python/src/pre_tokenizers.rs/0
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import pytest from tokenizers import BertWordPieceTokenizer from ..utils import bert_files, data_dir class TestEncoding: @pytest.fixture(scope="class") def encodings(self, bert_files): tokenizer = BertWordPieceTokenizer.from_file(bert_files["vocab"]) single_encoding = tokenizer.encode("I lov...
tokenizers/bindings/python/tests/bindings/test_encoding.py/0
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import pytest from tokenizers import SentencePieceBPETokenizer, SentencePieceUnigramTokenizer class TestSentencePieceBPE: def test_train_from_iterator(self): text = ["A first sentence", "Another sentence", "And a last one"] tokenizer = SentencePieceBPETokenizer() tokenizer.train_from_iter...
tokenizers/bindings/python/tests/implementations/test_sentencepiece.py/0
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# Trainers <tokenizerslangcontent> <python> ## BpeTrainer [[autodoc]] tokenizers.trainers.BpeTrainer ## UnigramTrainer [[autodoc]] tokenizers.trainers.UnigramTrainer ## WordLevelTrainer [[autodoc]] tokenizers.trainers.WordLevelTrainer ## WordPieceTrainer [[autodoc]] tokenizers.trainers.WordPieceTrainer </python...
tokenizers/docs/source-doc-builder/api/trainers.mdx/0
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/* Our DOM objects */ /* Version control */ .selectors { margin-bottom: 10px; } .dropdown-button { display: inline-block; width: 50%; background-color: #6670FF; color: white; border: none; padding: 5px; font-size: 15px; cursor: pointer; } .dropdown-button:hover, .dropdown-button:...
tokenizers/docs/source/_static/css/huggingface.css/0
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Training from memory ---------------------------------------------------------------------------------------------------- In the `Quicktour <quicktour>`__, we saw how to build and train a tokenizer using text files, but we can actually use any Python Iterator. In this section we'll see a few different ways of training...
tokenizers/docs/source/tutorials/python/training_from_memory.rst/0
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[package] name = "unstable_wasm" version = "0.1.0" authors = ["Nicolas Patry"] edition = "2018" [lib] crate-type = ["cdylib", "rlib"] [features] default = ["console_error_panic_hook"] [dependencies] wasm-bindgen = "0.2.63" # The `console_error_panic_hook` crate provides better debugging of panics by # logging them ...
tokenizers/tokenizers/examples/unstable_wasm/Cargo.toml/0
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const CopyWebpackPlugin = require("copy-webpack-plugin"); const path = require('path'); module.exports = { entry: "./bootstrap.js", output: { path: path.resolve(__dirname, "dist"), filename: "bootstrap.js", }, mode: "development", plugins: [ new CopyWebpackPlugin(['index.html']) ], };
tokenizers/tokenizers/examples/unstable_wasm/www/webpack.config.js/0
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//! Popular tokenizer models. pub mod bpe; pub mod unigram; pub mod wordlevel; pub mod wordpiece; use ahash::AHashMap; use std::collections::HashMap; use std::path::{Path, PathBuf}; use serde::{Deserialize, Deserializer, Serialize, Serializer}; use crate::models::bpe::{BpeTrainer, BPE}; use crate::models::unigram::...
tokenizers/tokenizers/src/models/mod.rs/0
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use crate::tokenizer::{NormalizedString, Normalizer, Result}; pub use spm_precompiled::Precompiled; use std::cmp::Ordering; use unicode_segmentation::UnicodeSegmentation; fn replace(transformations: &mut Vec<(char, isize)>, old_part: &str, new_part: &str) { let old_count = old_part.chars().count() as isize; le...
tokenizers/tokenizers/src/normalizers/precompiled.rs/0
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mod pre_tokenizer; mod scripts; // Re-export the PreTokenizer pub use pre_tokenizer::UnicodeScripts;
tokenizers/tokenizers/src/pre_tokenizers/unicode_scripts/mod.rs/0
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use ahash::AHashMap; use std::borrow::Borrow; use std::hash::Hash; use std::sync::RwLock; /// The default capacity for a `BPE`'s internal cache. pub static DEFAULT_CACHE_CAPACITY: usize = 10_000; /// The maximum length we should cache in a model /// Strings that are too long have minimal chances to cache hit anyway pu...
tokenizers/tokenizers/src/utils/cache.rs/0
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use tokenizers::{ normalizers, pre_tokenizers::split::{Split, SplitPattern}, AddedToken, NormalizerWrapper, PreTokenizerWrapper, SplitDelimiterBehavior, Tokenizer, }; #[test] fn test_decoding_with_added_bpe() { let mut tokenizer = Tokenizer::from_file("data/llama-3-tokenizer.json").unwrap(); tokeni...
tokenizers/tokenizers/tests/stream.rs/0
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# Server-side Audio Processing in Node.js A major benefit of writing code for the web is that you can access the multitude of APIs that are available in modern browsers. Unfortunately, when writing server-side code, we are not afforded such luxury, so we have to find another way. In this tutorial, we will design a si...
transformers.js/docs/source/guides/node-audio-processing.md/0
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import { useState, useRef, useEffect } from "react"; import Editor from "@monaco-editor/react"; import Progress from './components/Progress'; import './App.css' const MODELS = [ 'Xenova/tiny_starcoder_py', 'Xenova/codegen-350M-mono', // 'Xenova/starcoderbase-1b', ] function App() { // Editor setup const m...
transformers.js/examples/code-completion/src/App.jsx/0
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@tailwind base; @tailwind components; @tailwind utilities;
transformers.js/examples/cross-encoder/src/index.css/0
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import { useEffect, useState, useRef, useCallback } from 'react'; import Progress from './components/Progress'; import ImageInput from './components/ImageInput'; const IS_WEBGPU_AVAILABLE = !!navigator.gpu; function App() { // Create a reference to the worker object. const worker = useRef(null); // Model loa...
transformers.js/examples/florence2-webgpu/src/App.jsx/0
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import { pipeline } from "@xenova/transformers"; // Use the Singleton pattern to enable lazy construction of the pipeline. // NOTE: We wrap the class in a function to prevent code duplication (see below). const P = () => class PipelineSingleton { static task = 'text-classification'; static model = 'Xenova/dist...
transformers.js/examples/next-server/src/app/classify/pipeline.js/0
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import { decode } from 'blurhash' const SIZE = 32; export function blurHashToDataURL(hash) { if (!hash) return undefined const pixels = decode(hash, SIZE, SIZE) const canvas = document.createElement("canvas"); canvas.width = SIZE; canvas.height = SIZE; const ctx = canvas.getContext("2d"); ...
transformers.js/examples/semantic-image-search-client/src/app/utils.js/0
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import { AutoTokenizer, CLIPTextModelWithProjection } from "@xenova/transformers"; import { createClient } from '@supabase/supabase-js' // Use the Singleton pattern to enable lazy construction of the pipeline. // NOTE: We wrap the class in a function to prevent code duplication (see below). const S = () => class Appli...
transformers.js/examples/semantic-image-search/src/app/app.js/0
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import './style.css'; import { env, AutoModel, AutoProcessor, RawImage } from '@xenova/transformers'; // Since we will download the model from the Hugging Face Hub, we can skip the local model check env.allowLocalModels = false; // Proxy the WASM backend to prevent the UI from freezing env.backends.onnx.wasm.proxy =...
transformers.js/examples/video-object-detection/main.js/0
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<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <title>Transformers.js | WebGPU Benchmark</title> </head> <body> <h1> <a href="https://github.com/huggingface/transformers.js" target="_blank">🤗 Transformers.js</a> We...
transformers.js/examples/webgpu-embedding-benchmark/index.html/0
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export default function ImageIcon(props) { return ( <svg {...props} xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2" stro...
transformers.js/examples/webgpu-vlm/src/components/icons/ImageIcon.jsx/0
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import { useEffect, useState, useRef } from 'react'; import { AudioVisualizer } from './components/AudioVisualizer'; import Progress from './components/Progress'; import { LanguageSelector } from './components/LanguageSelector'; const IS_WEBGPU_AVAILABLE = !!navigator.gpu; const WHISPER_SAMPLING_RATE = 16_000; const...
transformers.js/examples/webgpu-whisper/src/App.jsx/0
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function titleCase(str) { str = str.toLowerCase(); return (str.match(/\w+.?/g) || []) .map((word) => { return word.charAt(0).toUpperCase() + word.slice(1); }) .join(""); } // List of supported languages: // https://help.openai.com/en/articles/7031512-whisper-api-faq // http...
transformers.js/examples/whisper-word-timestamps/src/components/LanguageSelector.jsx/0
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import { useState, useRef, useEffect, useCallback } from 'react' import './App.css' const PLACEHOLDER_REVIEWS = [ // battery/charging problems "Disappointed with the battery life! The phone barely lasts half a day with regular use. Considering how much I paid for it, I expected better performance in this departmen...
transformers.js/examples/zero-shot-classification/src/App.jsx/0
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/** * @module generation/logits_sampler */ import { Callable } from "../utils/generic.js"; import { Tensor, topk } from "../utils/tensor.js"; import { max, softmax, } from '../utils/maths.js'; import { GenerationConfig } from '../generation/configuration_utils.js'; /** * Sampler is a base class for all s...
transformers.js/src/generation/logits_sampler.js/0
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import { ImageProcessor, } from "../../base/image_processors_utils.js"; export class DeiTImageProcessor extends ImageProcessor { } export class DeiTFeatureExtractor extends DeiTImageProcessor { }
transformers.js/src/models/deit/image_processing_deit.js/0
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export * from './beit/image_processing_beit.js' export * from './bit/image_processing_bit.js' export * from './chinese_clip/image_processing_chinese_clip.js' export * from './clip/image_processing_clip.js' export * from './convnext/image_processing_convnext.js' export * from './deit/image_processing_deit.js' export * ...
transformers.js/src/models/image_processors.js/0
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import { Processor } from "../../base/processing_utils.js"; import { AutoImageProcessor } from "../auto/image_processing_auto.js"; export class SamProcessor extends Processor { static image_processor_class = AutoImageProcessor async _call(...args) { return await this.image_processor(...args); } ...
transformers.js/src/models/sam/processing_sam.js/0
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import { FeatureExtractor, validate_audio_inputs } from "../../base/feature_extraction_utils.js"; import { Tensor } from "../../utils/tensor.js"; export class Wav2Vec2FeatureExtractor extends FeatureExtractor { /** * @param {Float32Array} input_values * @returns {Float32Array} */ _zero_mean_u...
transformers.js/src/models/wav2vec2/feature_extraction_wav2vec2.js/0
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/** * @file Custom data structures. * * These are only used internally, meaning an end-user shouldn't * need to access anything here. * * @module utils/data-structures */ /** * Efficient Heap-based Implementation of a Priority Queue. * It uses an array-based binary heap, where the root is at index `0`, an...
transformers.js/src/utils/data-structures.js/0
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import { AutoImageProcessor, Qwen2VLImageProcessor } from "../../../src/transformers.js"; import { load_cached_image } from "../../asset_cache.js"; import { MAX_PROCESSOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js"; export default () => { // Qwen2VLImageProcessor // - custom image processing (min_pi...
transformers.js/tests/models/qwen2_vl/test_image_processing_qwen2_vl.js/0
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import { Wav2Vec2CTCTokenizer } from "../../../src/tokenizers.js"; import { BASE_TEST_STRINGS, BERT_TEST_STRINGS } from "../test_strings.js"; export const TOKENIZER_CLASS = Wav2Vec2CTCTokenizer; export const TEST_CONFIG = { "Xenova/wav2vec2-base-960h": { SIMPLE: { text: BASE_TEST_STRINGS.SIMPLE, toke...
transformers.js/tests/models/wav2vec2/test_tokenization_wav2vec2.js/0
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import { pipeline, ImageFeatureExtractionPipeline } from "../../src/transformers.js"; import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js"; import { load_cached_image } from "../asset_cache.js"; const PIPELINE_ID = "image-feature-extraction"; export...
transformers.js/tests/pipelines/test_pipelines_image_feature_extraction.js/0
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import { pipeline, ZeroShotObjectDetectionPipeline } from "../../src/transformers.js"; import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js"; import { load_cached_image } from "../asset_cache.js"; const PIPELINE_ID = "zero-shot-object-detection"; exp...
transformers.js/tests/pipelines/test_pipelines_zero_shot_object_detection.js/0
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version: 2.1 setup: true orbs: continuation: circleci/continuation@0.1.0 parameters: nightly: type: boolean default: false GHA_Actor: type: string default: "" GHA_Action: type: string default: "" GHA_Event: type: string default: "" ...
transformers/.circleci/config.yml/0
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<!--- Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or ...
transformers/ISSUES.md/0
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import argparse import subprocess def main(config_dir, config_name, args): subprocess.run(["optimum-benchmark", "--config-dir", f"{config_dir}", "--config-name", f"{config_name}"] + ["hydra/job_logging=disabled", "hydra/hydra_logging=disabled"] + args) if __name__ == "__main__": parser = argparse.ArgumentPa...
transformers/benchmark/optimum_benchmark_wrapper.py/0
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FROM nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04 LABEL maintainer="Hugging Face" ARG DEBIAN_FRONTEND=noninteractive # Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands) SHELL ["sh", "-lc"] # The following `ARG` are mainly used to specify the versions explicit...
transformers/docker/transformers-quantization-latest-gpu/Dockerfile/0
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# استخدام مجزئيات النصوص من 🤗 Tokenizers يعتمد [`PreTrainedTokenizerFast`] على مكتبة [🤗 Tokenizers](https://huggingface.co/docs/tokenizers). يمكن تحميل المجزئات اللغويين الذين تم الحصول عليهم من مكتبة 🤗 Tokenizers ببساطة شديدة في 🤗 Transformers. قبل الدخول في التفاصيل، دعونا نبدأ أولاً بإنشاء مُجزىء لغوي تجريبي ف...
transformers/docs/source/ar/fast_tokenizers.md/0
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# التعقيد اللغوي للنماذج ذات الطول الثابت [[open-in-colab]] التعقيد اللغوي (PPL) هي واحدة من أكثر المقاييس شيوعًا لتقييم نماذج اللغة. قبل الخوض في التفاصيل، يجب أن نلاحظ أن المقياس ينطبق تحديدًا على نماذج اللغة الكلاسيكية (يُطلق عليها أحيانًا نماذج اللغة التلقائية المرجعية أو السببية) وهي غير محددة جيدًا لنماذج اللغ...
transformers/docs/source/ar/perplexity.md/0
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<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agree...
transformers/docs/source/ar/tasks/token_classification.md/0
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<!--- Copyright 2024 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or ...
transformers/docs/source/de/contributing.md/0
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<!--Copyright 2024 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/accelerate.md/0
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# Using Cursor as a client of transformers serve This example shows how to use `transformers serve` as a local LLM provider for [Cursor](https://cursor.com/), the popular IDE. In this particular case, requests to `transformers serve` will come from an external IP (Cursor's server IPs), which requires some additional s...
transformers/docs/source/en/cursor.md/0
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<!--- Copyright 2024 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or ...
transformers/docs/source/en/installation.md/0
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<!--Copyright 2023 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to...
transformers/docs/source/en/llm_tutorial_optimization.md/0
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<!--Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/main_classes/pipelines.md/0
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<!--Copyright 2025 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/model_doc/aya_vision.md/0
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### `tiny-agents` CLI and MCP Tools To showcase the use of MCP tools, let's see how to integrate the `transformers serve` server with the [`tiny-agents`](https://huggingface.co/blog/python-tiny-agents) CLI. > [!TIP] > Many Hugging Face Spaces can be used as MCP servers, as in this example. You can find all compatible...
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<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer. --> # 열심히 번역 중입니다. 조금 이따 만나요!
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# Cohere[[cohere]] ## 개요[[overview]] The Cohere Command-R 모델은 Cohere팀이 [Command-R: 프로덕션 규모의 검색 증강 생성](https://txt.cohere.com/command-r/)라는 블로그 포스트에서 소개 되었습니다. 논문 초록: *Command-R은 기업의 프로덕션 규모 AI를 가능하게 하기 위해 RAG(검색 증강 생성)와 도구 사용을 목표로 하는 확장 가능한 생성 모델입니다. 오늘 우리는 대규모 프로덕션 워크로드를 목표로 하는 새로운 LLM인 Command-R을 소개합니다. Command-R...
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